From 37f6aa67c4b43f2f514f7f7f5da8f85294d1be60 Mon Sep 17 00:00:00 2001 From: Joe Carstairs Date: Mon, 13 May 2024 14:10:08 +0100 Subject: [PATCH 01/22] Centre-aligns images in blog posts --- scss/_default-post.scss | 5 +++++ style.css | 2 +- 2 files changed, 6 insertions(+), 1 deletion(-) diff --git a/scss/_default-post.scss b/scss/_default-post.scss index ad5a848ebb..2a7691b340 100644 --- a/scss/_default-post.scss +++ b/scss/_default-post.scss @@ -117,6 +117,11 @@ ul { list-style-type: disc; } + + img { + display: block; + margin-inline: auto; + } } .author-information { diff --git a/style.css b/style.css index 2f24b92c5d..59b664eb6a 100644 --- a/style.css +++ b/style.css @@ -1 +1 @@ -applause-button{position:relative;cursor:pointer}applause-button .style-root{position:absolute;top:0;left:0;right:0;bottom:0}applause-button .style-root:after{position:absolute;top:0;left:0;right:0;bottom:0;content:" ";display:block;border-radius:50%;border:1px solid}applause-button 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.count{color:inherit;width:60px !important}applause-button .style-root:before{position:absolute;top:0;left:0;right:0;bottom:0;content:" ";display:block;border-radius:50%;background-color:#fff}.cc-window{box-shadow:0px 2px 4px 0px rgba(0,0,0,.12),0px 2px 6px 0px rgba(0,0,0,.1);width:22em;max-width:22em;font-family:"objektiv-mk1",Helvetica,Arial,sans-serif !important;font-size:12px}.cc-link{font-weight:normal}@media screen and (max-width: 900px){.cc-window.cc-left{left:50%;transform:translate(-50%)}.cc-window.cc-bottom{bottom:20px}}table thead{border-bottom:1px solid #333}table th{font-weight:bold}table td,table th{padding:5px}table tr:first-child td{padding-top:10px}th:nth-child(5),td:nth-child(5){border-left:1px solid gray}table{margin-bottom:17px} From bb2406ed22743cd4963e38f01b4976dd2a1fc3c8 Mon Sep 17 00:00:00 2001 From: Joe Carstairs Date: Wed, 31 Jul 2024 17:11:53 +0100 Subject: [PATCH 02/22] LLMs don't hallucinate --- _data/authors.yml | 5 + _posts/2024-08-29-llms-dont-hallucinate.md | 458 ++++++++++++++++++ jcarstairs/assets/ai-safety-summit.jpg | Bin 0 -> 140271 bytes jcarstairs/assets/crumple-zone.jpg | Bin 0 -> 311601 bytes jcarstairs/assets/dice.jpg | Bin 0 -> 76055 bytes jcarstairs/assets/mirage.jpg | Bin 0 -> 99742 bytes .../assets/picture-falling-off-wall.jpg | Bin 0 -> 80999 bytes jcarstairs/assets/predictive-text.jpg | Bin 0 -> 38033 bytes jcarstairs/assets/whisky.jpg | Bin 0 -> 117613 bytes jcarstairs/atom.xml | 4 + jcarstairs/feed.xml | 4 + jcarstairs/index.html | 5 + jcarstairs/picture.webp | Bin 0 -> 29186 bytes 13 files changed, 476 insertions(+) create mode 100644 _posts/2024-08-29-llms-dont-hallucinate.md create mode 100644 jcarstairs/assets/ai-safety-summit.jpg create mode 100644 jcarstairs/assets/crumple-zone.jpg create mode 100644 jcarstairs/assets/dice.jpg create mode 100644 jcarstairs/assets/mirage.jpg create mode 100644 jcarstairs/assets/picture-falling-off-wall.jpg create mode 100644 jcarstairs/assets/predictive-text.jpg create mode 100644 jcarstairs/assets/whisky.jpg create mode 100644 jcarstairs/atom.xml create mode 100644 jcarstairs/feed.xml create mode 100644 jcarstairs/index.html create mode 100644 jcarstairs/picture.webp diff --git a/_data/authors.yml b/_data/authors.yml index f67019e663..227cfe0286 100644 --- a/_data/authors.yml +++ b/_data/authors.yml @@ -59,6 +59,7 @@ active-authors: - jarnstein - jbeckles - jbickleywallace + - jcarstairs - jcwright - jdunleavy - jgrant @@ -502,6 +503,10 @@ authors: picture: picture.jpg jcardy: name: "Jonathan Cardy" + jcarstairs: + name: "Joe Carstairs" + author-summary: "

Developer at Scott Logic. Find me on joeac.net.

" + picture: picture.webp jcwright: name: "Jay Wright" author-summary: "

I'm a developer at Scott Logic, based in Newcastle. My primary interest is in backend development, primarily in Java, and cloud technologies. I also have experience building web applications in React.

\n

Away from work I'm a keen petrol head and avid Formula One fan. I support McLaren, in case you were wondering.

" diff --git a/_posts/2024-08-29-llms-dont-hallucinate.md b/_posts/2024-08-29-llms-dont-hallucinate.md new file mode 100644 index 0000000000..21a268a80c --- /dev/null +++ b/_posts/2024-08-29-llms-dont-hallucinate.md @@ -0,0 +1,458 @@ +--- +author: jcarstairs +title: LLMs don't 'hallucinate' +date: 2024-08-29 00:00:00 Z +summary: Describing LLMs as 'hallucinating' fundamentally distorts how LLMs work. We can do better. +category: AI +image: "/jcarstairs/assets/mirage.jpg" +layout: default_post +--- + +It's well known that LLMs make stuff up. + +LLMs can confidently tell you all about +[the winners of non-existent sporting events](https://medium.com/@gcentulani/understanding-hallucination-in-llms-causes-consequences-and-mitigation-strategies-b5e1d0268069). +They can [invent legal cases](https://www.bbc.co.uk/news/world-us-canada-65735769), +and [fabricate fake science](https://www.technologyreview.com/2022/11/18/1063487/meta-large-language-model-ai-only-survived-three-days-gpt-3-science/). +These kinds of outputs are often called 'hallucinations'. + +

+

+ +
+ A desert mirage. + Photo credit: Ashabot, Wikimedia, + CC BY-SA 4.0 +
+
+

+ +Thankfully, the greatest minds of the human race are hard at work ironing out +this problem. Solutions abound: +[improving the quality of your training data](https://medium.com/@gcentulani/understanding-hallucination-in-llms-causes-consequences-and-mitigation-strategies-b5e1d0268069), +[testing the system diligently](https://www.ibm.com/topics/ai-hallucinations), +[fine-tuning the model with human feedback](https://openai.com/index/gpt-4/), +[integrating external data sources](https://arxiv.org/pdf/2104.07567), +or even just +[asking the LLM to evaluate its own answer](https://aclanthology.org/2023.findings-emnlp.123.pdf) +are just some of the ingeneous techniques engineers and scientists have +proposed. + +According to OpenAI, +[GPT-4 reduces hallucination by 40%](https://openai.com/index/gpt-4) compared +to its predecessors: and surely, LLM proponents say, that pattern will continue, +Moore's-Law-style, until LLM hallucination is a distant memory, like the +[inefficiencies in early steam engines](https://www.thehistorycorner.org/articles-by-the-team/a-dream-too-lofty-why-the-steam-engine-failed-in-the-18th-century). + +But is it so simple? A growing chorus of academics, engineers and journalists +are calling this narrative in this question.[^3] Maybe hallucination isn't a +solvable problem at all. + +

+

+ +
+ Taken at the AI Safety Summit hosted at Bletchley Park, Nov 2023. + Photo credit: Marcel Grabowski, + CC BY-NC-ND 2.0 +
+
+

+ +Indeed, it's increasingly clear that the word 'hallucination' fundamentally +distorts the way LLMs actually work. This means our judgements of what LLMs are +capable of could be systematically off the mark. + +I think we need to do better. What we need is **a new word**, one which captures +how LLMs behave without baking in false assumptions about how they work. But +before we get to the solution, we need to properly understand the problem. + +## What is 'hallucination'? + +Since about 2017, researchers in natural language processing have used the word +'hallucination' to describe a variety of behaviours. Since ChatGPT propelled +LLMs to public awareness, this word has become commonplace. But what does it +actually mean?[^1] + +Sometimes, it refers to **unfaithfulness**, a jargon word in natural language +processing. This means that its output contains information which is not +epistemically supported[^6] by the input. + +For example, if you try to get an LLM to summarise a meeting transcript, and its +summary includes events which weren't described in the transcript, the LLM has +been 'unfaithful'. + +When academics talk about 'hallucination', they most often mean something like +'unfaithfulness' - though other meanings are common, too. + +Sometimes, 'hallucination' refers **unfactuality**: producing outputs which +contain falsehoods, or other falsehood-like things, such as epistemically +unjustifiable claims, or claims which contradict the LLM's training data. This +is probably the most common usage in non-academic circles. + +By the way, sometimes, 'hallucination' has another meaning altogether: +**nonsense**. Nonsense is one of those things which is hard to define, but you +know it when you see it. This isn't my focus for this article, so let's leave +nonsense aside for proper treatment another day. + +So what people mean by 'hallucination' is pretty diverse. But all of these +different usages have something in common: 'hallucination' is regarded as +**abnormal behaviour**. + +### 'Hallucination' as abnormal behaviour: what this means + +Treating hallucination as abnormal is very significant. Let's take a minute to +consider what this entails. + +First of all, it entails that **every hallucination can be explained**, at least +in principle. + +This is because, when the environment and inputs are normal, systems behave +normally. So, whenever an LLM hallucinates, we should expect to find something +abnormal about the environment or the input which explains why the LLM +hallucinated. + +Secondly, if hallucination is abnormal behaviour, that implies that not +hallucinating is normal. + +That in turn means **there must be some mechanism**, some causal explanation +contained in how LLMs work, which explains why, under normal conditions, LLMs +don't hallucinate. + +These points are pretty subtle, so let's explore a couple of examples. + +Think about dice. I could roll six sixes in a row, and that would be really +unusual behaviour for the die. + +

+

+ +
+ Photo credit: barefootliam-stock, + CC BY 3.0 +
+
+

+ +But firstly, this wouldn't demand an explanation. It's not a bug, it's just +statistically improbable. + +And secondly, there's no mechanism which can explain why, under normal +conditions, a die shouldn't roll six sixes in a row. Very, very occasionally, +it's just what dice do. + +That's because it's normal (albeit uncommon) behaviour for a die to roll six +sixes in a row. + +In contrast, if I secure a picture to the wall with a picture hook, and it +falls off, I can reasonably expect there to be an explanation. + +

+

+ +
+ A picture hook behaving abnormally. Mind you, the chap on the ladder + appears to have bigger problems right now. + Photo credit: lookandlearn.com, + CC BY 4.0 +
+
+

+ +Maybe the nail was bent. Maybe I didn't hammer it in straight. Maybe I didn't +quite catch the string on the hook when I put it up. Point is, something must +have gone wrong for the picture hook to behave in this way. + +And again, we should expect there to be a mechanism which can explain why under +normal conditions, pictures hooked to the wall don't fall off. + +And indeed there is such a mechanism: the hook catches the string and transfers +the weight of the picture to the wall, instead of leaving that weight to pull +the picture down towards the ground. + +That's because for the picture to fall off the wall is abnormal behaviour +for a picture hook. + +So, to recap, if LLM hallucination is abnormal behaviour, we should expect two +things: + +1. There should be some abnormal conditions which explain why LLMs hallucinate + in some cases but not others. +2. There should be some mechanism which explains why, under normal conditions, + LLMs don't hallucinate. + +## Why the word 'hallucination' distorts how LLMs work + +**The word 'hallucination' doesn't satisfy either of those two expectations** +listed above. Therefore, the word 'hallucination' fundamentally distorts how +LLMs really work. + +Firstly, when an LLM hallucinates, we should expect there to be some abnormal +conditions which explain why it did so. But often, **there is no such +explanation**. + +Occasionally, LLMs produce unfaithful and untrue content for no good reason. +Often, the problem goes away by making meaningless tweaks in the wording of the +prompt, or even simply by running the algorithm again. + +Secondly, if hallucination is abnormal behaviour, we should expect there to be +some mechanism which causes LLMs not to hallucinate under normal conditions. +But **there is no such mechanism**. + +LLMs are predictive text machines, not oracles. They are not trained to target +faithfulness. They are not trained to target truth. They are trained to guess +the most plausible next word in the given sequence. So the case where the LLM +'hallucinates' and the case where it doesn't are causally indistinguishable. + +

+

+ +
+ The solution given by LLMs is new. The problem of predictive text is not. + Photo credit: Richard Masoner, + CC BY-SA 2.0 +
+
+

+ +There were some important and difficult points there, so let's elaborate. + +By comparison, for humans to produce false or unfaithful data would indeed be +abnormal behaviour for humans. + +But there are explanations for why humans sometimes produce such outputs: maybe +they had an incentive to lie, or they were playing a game, or they were joking. +Or indeed, perhaps they 'hallucinating' in the traditional sense! + +In contrast, an LLM can never have an incentive to lie, can never play a game, +and can never tell a joke. It's a pile of matrices. + +And there are mechanisms which explain why humans normally don't produce +unfaithful or unfactual content. We have mental models of the external world. We +continually amend those models in response to evidence. We receive that evidence +through perception. And we use language, among other things, as a means of +communicating our mental models with other people. + +But an LLM doesn't have any of these mechanisms. It doesn't have a model of +the external world. It doesn't respond to evidence. It doesn't gather evidence +through perception (or, indeed, by any other means). And it doesn't attempt to +use language as a means of communicating information with other minds. + +So the concept 'hallucination' fundamentally mischaracterises the nature of LLMs +by assuming that false or unfaithful outputs are abnormal for LLMs. In fact, +such outputs, while rare (at least in recent models), are nonetheless normal, +like a dice rolling six sixes in a row. + +## Why this matters + +OK, so 'hallucination' is not a great word. It assumes that unfaithful or +unfactual output is abnormal output for an LLM, when in fact, this just isn't +true. + +But why does this matter? Can't we afford a little fudge? + +I think the use of this word has serious consequences. + +In academia, researchers are spilling considerable amounts of ink trying to +ascertain the causes of 'hallucination'[^5]. + +Not only is this enterprise wholly in vain, but it risks spreading the +misperception that hallucination is incidental, and will disappear once we find +better ways of designing LLMs. That in turn could lead decision-makers to invest +in LLM applications that just won't work, under the false impression that the +'hallucination' problem will disappear in future. + +This isn't a hypothetical concern. LLMs have been set to +[writing entire travel articles](https://arstechnica.com/information-technology/2023/08/microsoft-ai-suggests-food-bank-as-a-cannot-miss-tourist-spot-in-canada), +[making legal submissions](https://www.texaslawblog.law/2023/06/sanctions-handed-down-to-lawyers-who-cited-fake-cases-relying-on-chatgpt), +[providing customers with information on company policies](https://bc.ctvnews.ca/air-canada-s-chatbot-gave-a-b-c-man-the-wrong-information-now-the-airline-has-to-pay-for-the-mistake-1.6769454) +with catastrophic results. + +And think about some of these common or proposed use cases: + +- Helping write submissions to academic journals +- Generating meeting minutes +- 'Chatting with data' +- Summarising non-structured data in social research +- Writing computer code +- Question-answering +- All-purpose 'AI assistants' + +Given that LLMs will, rarely, but inevitably, output garbage, all of these will +need to be done with enormous care to avoid causing serious damage. + +Furthermore, there's a risk that LLMs may act as a so-called 'moral crumple +zone', effectively soaking up responsibility for failures on behalf of the +humans who were really responsible. And where there's a lack of accountability, +organisations are unable to make good decisions or learn from their +mistakes[^2]. + +

+

+ +
+ Just as crumple zones in a car is designed to absorb the impact of a + collision to protect its driver, so misplaced and misunderstood LLMs + could act to absorb responsibility from decision-makers. + Photo credit: Karrmann, Wikimedia, + CC BY-SA 3.0 +
+
+

+ + +## How to understand LLM behaviour besides 'hallucinating' + +So, the word 'hallucination' isn't fit for purpose. But we do legitimately need +ways of understanding how LLMs behave, including their tendencies to produce +false or unfaithful outputs. + +### Confabulation: a step forward, but still flawed + +One alternative suggestion is to +[refer to the behaviour as 'confabulation' instead of 'hallucination'](https://journals.plos.org/digitalhealth/article?id=10.1371/journal.pdig.0000388). + +While, as the authors linked above rightly point out, this does address some +of the problems with the word 'hallucination', it still has the fundamental +flaw we've been discussing. It still assumes that 'hallucination', or +'confabulation', is a causally distinguishable abnormal behaviour. + +Humans sometimes confabulate, but normally they don't. When humans do +confabulate, we can expect there to be an explanation, like a head injury or +mental illness. And we can expect there to be mechanisms in how the human mind +works which explains why humans don't normally confabulate. + +But as we have seen, this is not an accurate analogy for LLMs. For an LLM, it is +just as normal for it to produce outputs which are false or unfaithful outputs +as it is for it to produce outputs which are true or faithful. + +### Bulls\*\*t: a better solution + +However, three philosophers at the University of Glasgow have come up with an +ingenious solution. + +[**LLMs are bulls\*\*t machines**](https://link.springer.com/article/10.1007/s10676-024-09775-5). + +Please don't be alarmed. Despite appearances, 'bulls\*\*t' +is a technical term in analytic philosophy, developed in +[Harry Frankfurt's 2005 classic, _On Bulls\*\*t_](https://doi.org/10.2307/j.ctt7t4wr). +Following Frankfurt's original usage, it's generally taken to mean something +like this: + +> **bulls\*\*tting**: speaking with a reckless disregard for the truth. + +Canonical examples include: + +- Pretending you know something about whisky to try and impress a client, + colleague, or crush +- Bizarre advertising slogans, like 'Red Bull gives you wings' +- Any time Paul Merton opens his mouth on [Just A Minute](https://www.bbc.co.uk/programmes/b006s5dp) + +Why is this a good match for describing how LLMs behave? Well, LLMs are trained +to imitate language patterns from its training data. The LLM has succeeded at +its task when its output is linguistically plausible. Not when it's true, +well-evidenced, or faithful to the input - just when it's linguistically +plausible. In other words, its goal is to 'sound like' what a human would say. + +This is just like what a human does when they bulls\*\*t. When someone +bulls\*\*ts, they're not aiming to produce anything true, faithful or coherent, +they're just trying to sound like they know what they're talking about. If I +can convince my crush that I'm a sophisticated gentleman that knows his Islays +from his Arrans, she just might consider going on a date with me. Actually +saying something true or meaningful about whisky is not the task at hand. + +

+

+ +
+ I like the long notes of smoke and spices in this one. Mmm, gingery. + Photo credit: Pixabay, + CC CC0 1.0 +
+
+

+ +The word 'bulls\*\*t' is a huge step forward: + +- **It captures LLMs' tendency to produce false or unfaithful output.** Since + truth and faithfulness are not goals, it's perfectly possible for it to + produce false or unfaithful output purely by chance. +- **It explains why LLMs often produce true and faithful output**, even if it is + accidental. After all, the best way to sound like you know what you're talking + about is to actually say true things! +- **It correctly identifies false and unfaithful outputs as normal for an LLM** + - unlike 'hallucination'. + +Previously, we thought that an LLM reliably outputs true and faithful content, +except when it occasionally hallucinates. Now, we recognise that an LLM in fact +always outputs bulls\*\*t. + +### The benefits of bulls\*\*t + +So far, the word 'hallucination' has trapped us into talking pretty negatively +about LLMs. But the word 'bulls\*\*t', perhaps surprisingly, frees us to talk +honestly about the positive applications of LLMs as well. + +As several authors have pointed out before[^4], the tendency of LLMs to produce +false and misleading output corresponds closely to its tendency to produce +output which might be described, for want of a better word, as 'creative'. + +Trying to understand why 'hallucinating' goes together with 'creativity' is a +profound mystery. But it's obvious that 'bulls\*\*tting' is in some sense a +'creative' activity by nature. (Just ask Paul Merton!) + +Therefore, because we have a more accurate description of how LLMs work, we +can make more robust decisions both about where LLM behaviour is desirable, and +about where it isn't. + +Would you employ a professional bulls\*\*tter to write your company minutes? Or +write large chunks of computer code? Or summarise a legal contract? + +Or what about redrafting an email in a formal tone? Or suggesting a more +descriptive variable name in your code? Or suggesting a couple extra Acceptance +Criteria on your Scrum Issue that you might have forgotten? Are these +applications where 'sounds about right' is good enough? + +## Recap + +The word 'hallucination' has gripped the popular narrative about LLMs. But the +word was never suited to describing how LLMs behave, and using it puts us at +risk of making bad bets on how LLMs ought to be applied. + +By using the word 'bulls\*\*t', we recognise that while false and unfaithful +outputs may be rare, they should occasionally be expected as part of the normal +operation of the LLM. + +As a result, we are empowered to recognise the applications where LLMs can +really make a positive difference, as well as quickly recognise the applications +where it can't. + +## Footnotes + +[^1]: If you want a fuller treatment of how the word 'hallucination' has been used historically in the field, see [my personal blog post](https://joeac.net/blog/2024/07/16/word_hallucination_with_reference_to_llms) on the topic. +[^2]: For more on this idea, look into [Madeleine Clare Elish's concept of moral crumple zones](https://doi.org/10.17351/ests2019.260). +[^3]: For example, see [a Harvard study on hallucination in the legal context](https://doi.org/10.48550/arXiv.2405.20362), this [philosophical article](https://arxiv.org/pdf/2212.03551) and this [Forbes article](https://www.forbes.com/sites/lanceeliot/2022/08/24/ai-ethics-lucidly-questioning-this-whole-hallucinating-ai-popularized-trend-that-has-got-to-stop) expressing concern about the use of the word 'hallucination', this [BBC journalist on using LLMs in journalism](https://www.bbc.co.uk/rd/blog/2024-06-mitigating-llm-hallucinations-in-text-summarisation), and this [PBS article which quotes an academic saying 'this isn't fixable'](https://www.pbs.org/newshour/science/chatbots-can-make-things-up-can-we-fix-ais-hallucination-problem). +[^4]: See for example [Jiang, Xuhui et al 2024. A Survey on Large Language Model Hallucination via a Creativity Perspective](https://arxiv.org/abs/2402.06647). +[^5]: See for example [Huang, Lei et al 2023. A Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open Questions](https://arxiv.org/abs/2311.05232). +[^6]: One thing 'epistemically supports' another thing just in case, if you already knew the first thing, you'd have good reason to believe the second thing, too. 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2024 09:24:21 +0100 Subject: [PATCH 03/22] Minor style/grammar/punctuation fixes --- _posts/2024-08-29-llms-dont-hallucinate.md | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/_posts/2024-08-29-llms-dont-hallucinate.md b/_posts/2024-08-29-llms-dont-hallucinate.md index 21a268a80c..bb0034d347 100644 --- a/_posts/2024-08-29-llms-dont-hallucinate.md +++ b/_posts/2024-08-29-llms-dont-hallucinate.md @@ -280,7 +280,7 @@ in LLM applications that just won't work, under the false impression that the This isn't a hypothetical concern. LLMs have been set to [writing entire travel articles](https://arstechnica.com/information-technology/2023/08/microsoft-ai-suggests-food-bank-as-a-cannot-miss-tourist-spot-in-canada), [making legal submissions](https://www.texaslawblog.law/2023/06/sanctions-handed-down-to-lawyers-who-cited-fake-cases-relying-on-chatgpt), -[providing customers with information on company policies](https://bc.ctvnews.ca/air-canada-s-chatbot-gave-a-b-c-man-the-wrong-information-now-the-airline-has-to-pay-for-the-mistake-1.6769454) +and [providing customers with information on company policies](https://bc.ctvnews.ca/air-canada-s-chatbot-gave-a-b-c-man-the-wrong-information-now-the-airline-has-to-pay-for-the-mistake-1.6769454) with catastrophic results. And think about some of these common or proposed use cases: @@ -296,7 +296,7 @@ And think about some of these common or proposed use cases: Given that LLMs will, rarely, but inevitably, output garbage, all of these will need to be done with enormous care to avoid causing serious damage. -Furthermore, there's a risk that LLMs may act as a so-called 'moral crumple +Furthermore, there's a risk that an LLM may act as a so-called 'moral crumple zone', effectively soaking up responsibility for failures on behalf of the humans who were really responsible. And where there's a lack of accountability, organisations are unable to make good decisions or learn from their @@ -399,10 +399,10 @@ The word 'bulls\*\*t' is a huge step forward: truth and faithfulness are not goals, it's perfectly possible for it to produce false or unfaithful output purely by chance. - **It explains why LLMs often produce true and faithful output**, even if it is - accidental. After all, the best way to sound like you know what you're talking - about is to actually say true things! + accidental. Plausible-sounding things, by their nature, have a good chance of + being true. - **It correctly identifies false and unfaithful outputs as normal for an LLM** - - unlike 'hallucination'. + \- unlike 'hallucination'. Previously, we thought that an LLM reliably outputs true and faithful content, except when it occasionally hallucinates. Now, we recognise that an LLM in fact From 53a9b5786edb6217dfd9eae0f252deed0406c145 Mon Sep 17 00:00:00 2001 From: Joe Carstairs Date: Thu, 29 Aug 2024 09:26:40 +0100 Subject: [PATCH 04/22] More minor style/grammar fixes --- _posts/2024-08-29-llms-dont-hallucinate.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/_posts/2024-08-29-llms-dont-hallucinate.md b/_posts/2024-08-29-llms-dont-hallucinate.md index bb0034d347..f66f23cd68 100644 --- a/_posts/2024-08-29-llms-dont-hallucinate.md +++ b/_posts/2024-08-29-llms-dont-hallucinate.md @@ -89,9 +89,9 @@ summary includes events which weren't described in the transcript, the LLM has been 'unfaithful'. When academics talk about 'hallucination', they most often mean something like -'unfaithfulness' - though other meanings are common, too. +'unfaithfulness' – though other meanings are common, too. -Sometimes, 'hallucination' refers **unfactuality**: producing outputs which +Sometimes, 'hallucination' refers to **unfactuality**: producing outputs which contain falsehoods, or other falsehood-like things, such as epistemically unjustifiable claims, or claims which contradict the LLM's training data. This is probably the most common usage in non-academic circles. From b20f6677ebca0bd8c35ee5be22e8569ac867fae7 Mon Sep 17 00:00:00 2001 From: Joe Carstairs Date: Thu, 29 Aug 2024 09:27:32 +0100 Subject: [PATCH 05/22] More minor style fixes --- _posts/2024-08-29-llms-dont-hallucinate.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/_posts/2024-08-29-llms-dont-hallucinate.md b/_posts/2024-08-29-llms-dont-hallucinate.md index f66f23cd68..2ef8b381a8 100644 --- a/_posts/2024-08-29-llms-dont-hallucinate.md +++ b/_posts/2024-08-29-llms-dont-hallucinate.md @@ -369,7 +369,7 @@ Canonical examples include: Why is this a good match for describing how LLMs behave? Well, LLMs are trained to imitate language patterns from its training data. The LLM has succeeded at its task when its output is linguistically plausible. Not when it's true, -well-evidenced, or faithful to the input - just when it's linguistically +well-evidenced, or faithful to the input – just when it's linguistically plausible. In other words, its goal is to 'sound like' what a human would say. This is just like what a human does when they bulls\*\*t. When someone From 84348381d05c85f8c4cd7f5a18cc194ea6f8f163 Mon Sep 17 00:00:00 2001 From: Joe Carstairs Date: Thu, 29 Aug 2024 09:34:08 +0100 Subject: [PATCH 06/22] More style improvements --- _posts/2024-08-29-llms-dont-hallucinate.md | 16 +++++++++------- 1 file changed, 9 insertions(+), 7 deletions(-) diff --git a/_posts/2024-08-29-llms-dont-hallucinate.md b/_posts/2024-08-29-llms-dont-hallucinate.md index 2ef8b381a8..7145ecc32c 100644 --- a/_posts/2024-08-29-llms-dont-hallucinate.md +++ b/_posts/2024-08-29-llms-dont-hallucinate.md @@ -8,13 +8,13 @@ image: "/jcarstairs/assets/mirage.jpg" layout: default_post --- -It's well known that LLMs make stuff up. +**LLMs make stuff up.** LLMs can confidently tell you all about [the winners of non-existent sporting events](https://medium.com/@gcentulani/understanding-hallucination-in-llms-causes-consequences-and-mitigation-strategies-b5e1d0268069). They can [invent legal cases](https://www.bbc.co.uk/news/world-us-canada-65735769), and [fabricate fake science](https://www.technologyreview.com/2022/11/18/1063487/meta-large-language-model-ai-only-survived-three-days-gpt-3-science/). -These kinds of outputs are often called 'hallucinations'. +These kinds of outputs are often called **hallucinations**.

    @@ -48,8 +48,8 @@ Moore's-Law-style, until LLM hallucination is a distant memory, like the [inefficiencies in early steam engines](https://www.thehistorycorner.org/articles-by-the-team/a-dream-too-lofty-why-the-steam-engine-failed-in-the-18th-century). But is it so simple? A growing chorus of academics, engineers and journalists -are calling this narrative in this question.[^3] Maybe hallucination isn't a -solvable problem at all. +are calling this narrative in this question.[^3] **Maybe hallucination isn't a +solvable problem at all.**

    @@ -69,9 +69,11 @@ Indeed, it's increasingly clear that the word 'hallucination' fundamentally distorts the way LLMs actually work. This means our judgements of what LLMs are capable of could be systematically off the mark. -I think we need to do better. What we need is **a new word**, one which captures -how LLMs behave without baking in false assumptions about how they work. But -before we get to the solution, we need to properly understand the problem. +**We need a new word**. We need a word which captures how LLMs behave, but +doesn't bake in false assumptions about how LLMs work. We need a word which +enables accurate and honest discussions about how best to apply LLMs. + +But before we get to the solution, we need to properly understand the problem. ## What is 'hallucination'? From f777b2142c9da779e1106991d4bf2f7a5641cd70 Mon Sep 17 00:00:00 2001 From: Joe Carstairs <118172583+jcarstairs-scottlogic@users.noreply.github.com> Date: Thu, 29 Aug 2024 15:00:38 +0100 Subject: [PATCH 07/22] Spelling: ingeneous -> ingenious Co-authored-by: lhancock-scottlogic <110816543+lhancock-scottlogic@users.noreply.github.com> --- _posts/2024-08-29-llms-dont-hallucinate.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/_posts/2024-08-29-llms-dont-hallucinate.md b/_posts/2024-08-29-llms-dont-hallucinate.md index 7145ecc32c..106a5aa953 100644 --- a/_posts/2024-08-29-llms-dont-hallucinate.md +++ b/_posts/2024-08-29-llms-dont-hallucinate.md @@ -38,7 +38,7 @@ this problem. Solutions abound: [integrating external data sources](https://arxiv.org/pdf/2104.07567), or even just [asking the LLM to evaluate its own answer](https://aclanthology.org/2023.findings-emnlp.123.pdf) -are just some of the ingeneous techniques engineers and scientists have +are just some of the ingenious techniques engineers and scientists have proposed. According to OpenAI, From eb34820133004577a1e25a411c98dd8931a71059 Mon Sep 17 00:00:00 2001 From: Joe Carstairs <118172583+jcarstairs-scottlogic@users.noreply.github.com> Date: Thu, 29 Aug 2024 15:01:31 +0100 Subject: [PATCH 08/22] Typo "calling this narrative in this question" -> "calling this narrative into question" Co-authored-by: lhancock-scottlogic <110816543+lhancock-scottlogic@users.noreply.github.com> --- _posts/2024-08-29-llms-dont-hallucinate.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/_posts/2024-08-29-llms-dont-hallucinate.md b/_posts/2024-08-29-llms-dont-hallucinate.md index 106a5aa953..a0fb76cc13 100644 --- a/_posts/2024-08-29-llms-dont-hallucinate.md +++ b/_posts/2024-08-29-llms-dont-hallucinate.md @@ -48,7 +48,7 @@ Moore's-Law-style, until LLM hallucination is a distant memory, like the [inefficiencies in early steam engines](https://www.thehistorycorner.org/articles-by-the-team/a-dream-too-lofty-why-the-steam-engine-failed-in-the-18th-century). But is it so simple? A growing chorus of academics, engineers and journalists -are calling this narrative in this question.[^3] **Maybe hallucination isn't a +are calling this narrative into question.[^3] **Maybe hallucination isn't a solvable problem at all.**

    From 87881632066e9079aa19c45a0d1880c1aaa593c2 Mon Sep 17 00:00:00 2001 From: Joe Carstairs Date: Thu, 29 Aug 2024 15:07:47 +0100 Subject: [PATCH 09/22] Moves first footnote --- _posts/2024-08-29-llms-dont-hallucinate.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/_posts/2024-08-29-llms-dont-hallucinate.md b/_posts/2024-08-29-llms-dont-hallucinate.md index a0fb76cc13..3266108065 100644 --- a/_posts/2024-08-29-llms-dont-hallucinate.md +++ b/_posts/2024-08-29-llms-dont-hallucinate.md @@ -78,9 +78,9 @@ But before we get to the solution, we need to properly understand the problem. ## What is 'hallucination'? Since about 2017, researchers in natural language processing have used the word -'hallucination' to describe a variety of behaviours. Since ChatGPT propelled +'hallucination' to describe a variety of behaviours[^1]. Since ChatGPT propelled LLMs to public awareness, this word has become commonplace. But what does it -actually mean?[^1] +actually mean? Sometimes, it refers to **unfaithfulness**, a jargon word in natural language processing. This means that its output contains information which is not From 4c7c4e4a00f314142b102ea76b0d35c393e5445a Mon Sep 17 00:00:00 2001 From: Joe Carstairs Date: Thu, 29 Aug 2024 15:21:07 +0100 Subject: [PATCH 10/22] Moves references in paragraph 2 to footnotes --- _posts/2024-08-29-llms-dont-hallucinate.md | 9 +++++---- 1 file changed, 5 insertions(+), 4 deletions(-) diff --git a/_posts/2024-08-29-llms-dont-hallucinate.md b/_posts/2024-08-29-llms-dont-hallucinate.md index 3266108065..b831174cf8 100644 --- a/_posts/2024-08-29-llms-dont-hallucinate.md +++ b/_posts/2024-08-29-llms-dont-hallucinate.md @@ -10,10 +10,8 @@ layout: default_post **LLMs make stuff up.** -LLMs can confidently tell you all about -[the winners of non-existent sporting events](https://medium.com/@gcentulani/understanding-hallucination-in-llms-causes-consequences-and-mitigation-strategies-b5e1d0268069). -They can [invent legal cases](https://www.bbc.co.uk/news/world-us-canada-65735769), -and [fabricate fake science](https://www.technologyreview.com/2022/11/18/1063487/meta-large-language-model-ai-only-survived-three-days-gpt-3-science/). +LLMs can confidently tell you all about the winners of non-existent sporting +events[^7], They can invent legal cases[^8], and fabricate fake science[^9]. These kinds of outputs are often called **hallucinations**.

    @@ -458,3 +456,6 @@ where it can't. [^4]: See for example [Jiang, Xuhui et al 2024. A Survey on Large Language Model Hallucination via a Creativity Perspective](https://arxiv.org/abs/2402.06647). [^5]: See for example [Huang, Lei et al 2023. A Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open Questions](https://arxiv.org/abs/2311.05232). [^6]: One thing 'epistemically supports' another thing just in case, if you already knew the first thing, you'd have good reason to believe the second thing, too. For example, that my flatmate, Sophie, is cross, provides epistemic support for the notion that her job interview didn't go well earlier today. +[^7]: Like when [an LLM made up the 1985 World Ski Championships](https://medium.com/@gcentulani/understanding-hallucination-in-llms-causes-consequences-and-mitigation-strategies-b5e1d0268069). +[^8]: As one person found to their misfortune, when [a New York lawyer filed a brief containing six references to non-existent cases](https://www.bbc.co.uk/news/world-us-canada-65735769). +[^9]: [Meta's AI academic assistant Galactica flopped after three days](https://www.technologyreview.com/2022/11/18/1063487/meta-large-language-model-ai-only-survived-three-days-gpt-3-science), following a backlash over its tendency to output falsehoods. From d03b94cfe354d8fcea7b8986307924ca37e08964 Mon Sep 17 00:00:00 2001 From: Joe Carstairs Date: Thu, 29 Aug 2024 15:24:38 +0100 Subject: [PATCH 11/22] Removes 'epistemically' --- _posts/2024-08-29-llms-dont-hallucinate.md | 9 ++++----- 1 file changed, 4 insertions(+), 5 deletions(-) diff --git a/_posts/2024-08-29-llms-dont-hallucinate.md b/_posts/2024-08-29-llms-dont-hallucinate.md index b831174cf8..20b71e148a 100644 --- a/_posts/2024-08-29-llms-dont-hallucinate.md +++ b/_posts/2024-08-29-llms-dont-hallucinate.md @@ -82,7 +82,7 @@ actually mean? Sometimes, it refers to **unfaithfulness**, a jargon word in natural language processing. This means that its output contains information which is not -epistemically supported[^6] by the input. +supported by the input. For example, if you try to get an LLM to summarise a meeting transcript, and its summary includes events which weren't described in the transcript, the LLM has @@ -92,9 +92,9 @@ When academics talk about 'hallucination', they most often mean something like 'unfaithfulness' – though other meanings are common, too. Sometimes, 'hallucination' refers to **unfactuality**: producing outputs which -contain falsehoods, or other falsehood-like things, such as epistemically -unjustifiable claims, or claims which contradict the LLM's training data. This -is probably the most common usage in non-academic circles. +contain falsehoods, or other falsehood-like things, such as unjustifiable +claims, or claims which contradict the LLM's training data. This is probably the +most common usage in non-academic circles. By the way, sometimes, 'hallucination' has another meaning altogether: **nonsense**. Nonsense is one of those things which is hard to define, but you @@ -455,7 +455,6 @@ where it can't. [^3]: For example, see [a Harvard study on hallucination in the legal context](https://doi.org/10.48550/arXiv.2405.20362), this [philosophical article](https://arxiv.org/pdf/2212.03551) and this [Forbes article](https://www.forbes.com/sites/lanceeliot/2022/08/24/ai-ethics-lucidly-questioning-this-whole-hallucinating-ai-popularized-trend-that-has-got-to-stop) expressing concern about the use of the word 'hallucination', this [BBC journalist on using LLMs in journalism](https://www.bbc.co.uk/rd/blog/2024-06-mitigating-llm-hallucinations-in-text-summarisation), and this [PBS article which quotes an academic saying 'this isn't fixable'](https://www.pbs.org/newshour/science/chatbots-can-make-things-up-can-we-fix-ais-hallucination-problem). [^4]: See for example [Jiang, Xuhui et al 2024. A Survey on Large Language Model Hallucination via a Creativity Perspective](https://arxiv.org/abs/2402.06647). [^5]: See for example [Huang, Lei et al 2023. A Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open Questions](https://arxiv.org/abs/2311.05232). -[^6]: One thing 'epistemically supports' another thing just in case, if you already knew the first thing, you'd have good reason to believe the second thing, too. For example, that my flatmate, Sophie, is cross, provides epistemic support for the notion that her job interview didn't go well earlier today. [^7]: Like when [an LLM made up the 1985 World Ski Championships](https://medium.com/@gcentulani/understanding-hallucination-in-llms-causes-consequences-and-mitigation-strategies-b5e1d0268069). [^8]: As one person found to their misfortune, when [a New York lawyer filed a brief containing six references to non-existent cases](https://www.bbc.co.uk/news/world-us-canada-65735769). [^9]: [Meta's AI academic assistant Galactica flopped after three days](https://www.technologyreview.com/2022/11/18/1063487/meta-large-language-model-ai-only-survived-three-days-gpt-3-science), following a backlash over its tendency to output falsehoods. From d39d158d11088cf2108851b6106675fb5c972632 Mon Sep 17 00:00:00 2001 From: Joe Carstairs Date: Thu, 29 Aug 2024 15:28:51 +0100 Subject: [PATCH 12/22] Simplifies 'have an incentive to lie' -> 'lie' --- _posts/2024-08-29-llms-dont-hallucinate.md | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/_posts/2024-08-29-llms-dont-hallucinate.md b/_posts/2024-08-29-llms-dont-hallucinate.md index 20b71e148a..d8720756f0 100644 --- a/_posts/2024-08-29-llms-dont-hallucinate.md +++ b/_posts/2024-08-29-llms-dont-hallucinate.md @@ -236,11 +236,11 @@ By comparison, for humans to produce false or unfaithful data would indeed be abnormal behaviour for humans. But there are explanations for why humans sometimes produce such outputs: maybe -they had an incentive to lie, or they were playing a game, or they were joking. -Or indeed, perhaps they 'hallucinating' in the traditional sense! +they were lying, or they were playing a game, or they were joking. Or indeed, +perhaps they 'hallucinating' in the traditional sense! -In contrast, an LLM can never have an incentive to lie, can never play a game, -and can never tell a joke. It's a pile of matrices. +In contrast, an LLM can never lie, can never play a game, and can never tell a +joke. It's a pile of matrices. And there are mechanisms which explain why humans normally don't produce unfaithful or unfactual content. We have mental models of the external world. We From b03e908a7f33051d15b562f5c170c2a4b1d832e2 Mon Sep 17 00:00:00 2001 From: Joe Carstairs <118172583+jcarstairs-scottlogic@users.noreply.github.com> Date: Thu, 29 Aug 2024 15:29:54 +0100 Subject: [PATCH 13/22] Style: 'unfactuality' -> 'non-factuality' Co-authored-by: lhancock-scottlogic <110816543+lhancock-scottlogic@users.noreply.github.com> --- _posts/2024-08-29-llms-dont-hallucinate.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/_posts/2024-08-29-llms-dont-hallucinate.md b/_posts/2024-08-29-llms-dont-hallucinate.md index d8720756f0..24e33ed984 100644 --- a/_posts/2024-08-29-llms-dont-hallucinate.md +++ b/_posts/2024-08-29-llms-dont-hallucinate.md @@ -91,7 +91,7 @@ been 'unfaithful'. When academics talk about 'hallucination', they most often mean something like 'unfaithfulness' – though other meanings are common, too. -Sometimes, 'hallucination' refers to **unfactuality**: producing outputs which +Sometimes, 'hallucination' refers to **non-factuality**: producing outputs which contain falsehoods, or other falsehood-like things, such as unjustifiable claims, or claims which contradict the LLM's training data. This is probably the most common usage in non-academic circles. From 0de672f5c14dc8e750e467587299b39339a37546 Mon Sep 17 00:00:00 2001 From: Joe Carstairs <118172583+jcarstairs-scottlogic@users.noreply.github.com> Date: Thu, 29 Aug 2024 16:17:00 +0100 Subject: [PATCH 14/22] Style: "the concept 'hallucination'" -> "the concept of 'hallucination'" Co-authored-by: lhancock-scottlogic <110816543+lhancock-scottlogic@users.noreply.github.com> --- _posts/2024-08-29-llms-dont-hallucinate.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/_posts/2024-08-29-llms-dont-hallucinate.md b/_posts/2024-08-29-llms-dont-hallucinate.md index 24e33ed984..151be75cfd 100644 --- a/_posts/2024-08-29-llms-dont-hallucinate.md +++ b/_posts/2024-08-29-llms-dont-hallucinate.md @@ -253,7 +253,7 @@ the external world. It doesn't respond to evidence. It doesn't gather evidence through perception (or, indeed, by any other means). And it doesn't attempt to use language as a means of communicating information with other minds. -So the concept 'hallucination' fundamentally mischaracterises the nature of LLMs +So the concept of 'hallucination' fundamentally mischaracterises the nature of LLMs by assuming that false or unfaithful outputs are abnormal for LLMs. In fact, such outputs, while rare (at least in recent models), are nonetheless normal, like a dice rolling six sixes in a row. From 1a9c6575b70fdd7383b7806e2b4d7084c70d213b Mon Sep 17 00:00:00 2001 From: Joe Carstairs Date: Thu, 29 Aug 2024 16:37:40 +0100 Subject: [PATCH 15/22] Moves more links to footnotes --- _posts/2024-08-29-llms-dont-hallucinate.md | 21 +++++++++++---------- 1 file changed, 11 insertions(+), 10 deletions(-) diff --git a/_posts/2024-08-29-llms-dont-hallucinate.md b/_posts/2024-08-29-llms-dont-hallucinate.md index 151be75cfd..0415908b5a 100644 --- a/_posts/2024-08-29-llms-dont-hallucinate.md +++ b/_posts/2024-08-29-llms-dont-hallucinate.md @@ -28,16 +28,12 @@ These kinds of outputs are often called **hallucinations**.

    -Thankfully, the greatest minds of the human race are hard at work ironing out -this problem. Solutions abound: -[improving the quality of your training data](https://medium.com/@gcentulani/understanding-hallucination-in-llms-causes-consequences-and-mitigation-strategies-b5e1d0268069), -[testing the system diligently](https://www.ibm.com/topics/ai-hallucinations), -[fine-tuning the model with human feedback](https://openai.com/index/gpt-4/), -[integrating external data sources](https://arxiv.org/pdf/2104.07567), -or even just -[asking the LLM to evaluate its own answer](https://aclanthology.org/2023.findings-emnlp.123.pdf) -are just some of the ingenious techniques engineers and scientists have -proposed. +Thankfully, the greatest minds of the human race are hard at work ironing +out this problem. Solutions abound: improving the quality of your training +data[^10], testing the system diligently[^11], fine-tuning the model with human +feedback[^12], integrating external data sources[^13], or even just asking the +LLM to evaluate its own answer[^14] are just some of the ingenious techniques +engineers and scientists have proposed. According to OpenAI, [GPT-4 reduces hallucination by 40%](https://openai.com/index/gpt-4) compared @@ -458,3 +454,8 @@ where it can't. [^7]: Like when [an LLM made up the 1985 World Ski Championships](https://medium.com/@gcentulani/understanding-hallucination-in-llms-causes-consequences-and-mitigation-strategies-b5e1d0268069). [^8]: As one person found to their misfortune, when [a New York lawyer filed a brief containing six references to non-existent cases](https://www.bbc.co.uk/news/world-us-canada-65735769). [^9]: [Meta's AI academic assistant Galactica flopped after three days](https://www.technologyreview.com/2022/11/18/1063487/meta-large-language-model-ai-only-survived-three-days-gpt-3-science), following a backlash over its tendency to output falsehoods. +[^10]: See for example [Gianluca Centulani on Medium](https://medium.com/@gcentulani/understanding-hallucination-in-llms-causes-consequences-and-mitigation-strategies-b5e1d0268069). +[^11]: See for example [IBM's introduction to LLM hallucination](https://www.ibm.com/topics/ai-hallucinations) +[^12]: As claimed by [OpenAI's GPT-4](https://openai.com/index/gpt-4). +[^13]: See for example [Shuster et al 2021. Retrieval Augmentation Reduces Hallucination in Conversation](https://arxiv.org/pdf/2104.07567). +[^14]: See for example [Ji et al 2024. Towards Mitigating Hallucination in Large Language Models via Self-Reflection](https://aclanthology.org/2023.findings-emnlp.123.pdf). From 47b4a1549b4a0a4cbc387f2e0e3e3777de63ae80 Mon Sep 17 00:00:00 2001 From: Joe Carstairs Date: Thu, 29 Aug 2024 16:40:39 +0100 Subject: [PATCH 16/22] Reference to personal blog for evidence on usage of the word 'hallucination' --- _posts/2024-08-29-llms-dont-hallucinate.md | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/_posts/2024-08-29-llms-dont-hallucinate.md b/_posts/2024-08-29-llms-dont-hallucinate.md index 0415908b5a..97a4124dc3 100644 --- a/_posts/2024-08-29-llms-dont-hallucinate.md +++ b/_posts/2024-08-29-llms-dont-hallucinate.md @@ -97,6 +97,10 @@ By the way, sometimes, 'hallucination' has another meaning altogether: know it when you see it. This isn't my focus for this article, so let's leave nonsense aside for proper treatment another day. +If you want to go deep on how people are using the word 'hallucination' to talk +about LLMs, I've investigated this in detail on +[my personal blog](https://joeac.net/blog/2024/07/16/word_hallucination_with_reference_to_llms). + So what people mean by 'hallucination' is pretty diverse. But all of these different usages have something in common: 'hallucination' is regarded as **abnormal behaviour**. From 2232d5acd9b4d7e9e72281d4a4d295b869af3087 Mon Sep 17 00:00:00 2001 From: Joe Carstairs Date: Fri, 30 Aug 2024 08:50:00 +0100 Subject: [PATCH 17/22] Optimises images --- _posts/2024-08-29-llms-dont-hallucinate.md | 16 ++++++++-------- jcarstairs/assets/ai-safety-summit.jpg | Bin 140271 -> 0 bytes jcarstairs/assets/ai-safety-summit.webp | Bin 0 -> 48482 bytes jcarstairs/assets/crumple-zone.jpg | Bin 311601 -> 0 bytes jcarstairs/assets/crumple-zone.webp | Bin 0 -> 84260 bytes jcarstairs/assets/dice.jpg | Bin 76055 -> 0 bytes jcarstairs/assets/dice.webp | Bin 0 -> 19364 bytes jcarstairs/assets/mirage.jpg | Bin 99742 -> 0 bytes jcarstairs/assets/mirage.webp | Bin 0 -> 13898 bytes jcarstairs/assets/picture-falling-off-wall.jpg | Bin 80999 -> 0 bytes .../assets/picture-falling-off-wall.webp | Bin 0 -> 71974 bytes jcarstairs/assets/predictive-text.jpg | Bin 38033 -> 0 bytes jcarstairs/assets/predictive-text.webp | Bin 0 -> 27092 bytes jcarstairs/assets/whisky.jpg | Bin 117613 -> 0 bytes jcarstairs/assets/whisky.webp | Bin 0 -> 50366 bytes 15 files changed, 8 insertions(+), 8 deletions(-) delete mode 100644 jcarstairs/assets/ai-safety-summit.jpg create mode 100644 jcarstairs/assets/ai-safety-summit.webp delete mode 100644 jcarstairs/assets/crumple-zone.jpg create mode 100644 jcarstairs/assets/crumple-zone.webp delete mode 100644 jcarstairs/assets/dice.jpg create mode 100644 jcarstairs/assets/dice.webp delete mode 100644 jcarstairs/assets/mirage.jpg create mode 100644 jcarstairs/assets/mirage.webp delete mode 100644 jcarstairs/assets/picture-falling-off-wall.jpg create mode 100644 jcarstairs/assets/picture-falling-off-wall.webp delete mode 100644 jcarstairs/assets/predictive-text.jpg create mode 100644 jcarstairs/assets/predictive-text.webp delete mode 100644 jcarstairs/assets/whisky.jpg create mode 100644 jcarstairs/assets/whisky.webp diff --git a/_posts/2024-08-29-llms-dont-hallucinate.md b/_posts/2024-08-29-llms-dont-hallucinate.md index 97a4124dc3..f16cf3ebb6 100644 --- a/_posts/2024-08-29-llms-dont-hallucinate.md +++ b/_posts/2024-08-29-llms-dont-hallucinate.md @@ -4,7 +4,7 @@ title: LLMs don't 'hallucinate' date: 2024-08-29 00:00:00 Z summary: Describing LLMs as 'hallucinating' fundamentally distorts how LLMs work. We can do better. category: AI -image: "/jcarstairs/assets/mirage.jpg" +image: "/jcarstairs/assets/mirage.webp" layout: default_post --- @@ -17,7 +17,7 @@ These kinds of outputs are often called **hallucinations**.

    @@ -48,7 +48,7 @@ solvable problem at all.**

    @@ -133,7 +133,7 @@ unusual behaviour for the die.

    @@ -159,7 +159,7 @@ falls off, I can reasonably expect there to be an explanation.

    @@ -219,7 +219,7 @@ the most plausible next word in the given sequence. So the case where the LLM

    @@ -305,7 +305,7 @@ mistakes[^2].

    @@ -382,7 +382,7 @@ saying something true or meaningful about whisky is not the task at hand.

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zr5!)XWk(MQk);Vgi8{MHle~kdsfDGKmy@ZA*Jo8@FKc6N6B0oIB0diu4_gOYQx`)b z4_g~MXC4oJ5wcUR~^#7m0Y#rp~{{^;lX80>i24j0$Mh`;=MrH;k zMqArI(f+0F>>}<4H2yn`|D(3Es;7e~qq3>9y{nV4skob|oeSwd<|fAf+B+5FW2 z6JthG8&g}LnlsQF^S`eCblSh#e;RCVY3uNp1(4Z)wOlOC{)d+TEuKF|{tEve=RmW6 z@%}f>f7p0IL+7$>5U9I*y-6! zjLhhbSWUR-O}P!Z*ttwiS=rdRNQg{~c_i$eYz={3W@&3^Zp!FxYUDsd^oLp=VMS?v z5*7xax4%9r+8DZ+0d;}n+S1O%-rf1{dR0qXQxzA(Kin~MvamBTu`zLTvaz#sG5=%v z#nj0eIJo|R{gq)PM1QE~0Y(qx(D2W=1DgKTCLU2IQ$rVfCsliU8-9|1v+_U3 zXu4aN{$Uiz0zD9wmFXWAI7t|Ru`~Wjb%7 literal 0 HcmV?d00001 From 9f24e61d35b3db34de290f20b4bc919c31e85ecc Mon Sep 17 00:00:00 2001 From: Joe Carstairs Date: Fri, 30 Aug 2024 09:01:33 +0100 Subject: [PATCH 18/22] Marks all child elements of figure as role=presentation The figures are purely decorative, so role=presentation is appropriate, but pa11y pointed out that assistive technology may still treat child elements of the figure as non-decorative, which is not what I intended. So I've marked all the children of the figures as role=presentation too. --- _posts/2024-08-29-llms-dont-hallucinate.md | 49 ++++++++++++---------- 1 file changed, 28 insertions(+), 21 deletions(-) diff --git a/_posts/2024-08-29-llms-dont-hallucinate.md b/_posts/2024-08-29-llms-dont-hallucinate.md index f16cf3ebb6..ef6a002b27 100644 --- a/_posts/2024-08-29-llms-dont-hallucinate.md +++ b/_posts/2024-08-29-llms-dont-hallucinate.md @@ -19,11 +19,12 @@ These kinds of outputs are often called **hallucinations**. -

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      +
      A desert mirage. - Photo credit: Ashabot, Wikimedia, - CC BY-SA 4.0 + Photo credit: Ashabot, Wikimedia, + CC BY-SA 4.0

    @@ -50,11 +51,12 @@ solvable problem at all.** -
    +
    Taken at the AI Safety Summit hosted at Bletchley Park, Nov 2023. - Photo credit: Marcel Grabowski, - CC BY-NC-ND 2.0 + Photo credit: Marcel Grabowski, + CC BY-NC-ND 2.0

    @@ -135,10 +137,11 @@ unusual behaviour for the die. -
    - Photo credit: barefootliam-stock, - CC BY 3.0 +
    + Photo credit: barefootliam-stock, + CC BY 3.0

    @@ -161,12 +164,13 @@ falls off, I can reasonably expect there to be an explanation. -
    +
    A picture hook behaving abnormally. Mind you, the chap on the ladder appears to have bigger problems right now. - Photo credit: lookandlearn.com, - CC BY 4.0 + Photo credit: lookandlearn.com, + CC BY 4.0

    @@ -221,11 +225,12 @@ the most plausible next word in the given sequence. So the case where the LLM -
    +
    The solution given by LLMs is new. The problem of predictive text is not. - Photo credit: Richard Masoner, - CC BY-SA 2.0 + Photo credit: Richard Masoner, + CC BY-SA 2.0

    @@ -307,13 +312,14 @@ mistakes[^2]. -
    +
    Just as crumple zones in a car is designed to absorb the impact of a collision to protect its driver, so misplaced and misunderstood LLMs could act to absorb responsibility from decision-makers. - Photo credit: Karrmann, Wikimedia, - CC BY-SA 3.0 + Photo credit: Karrmann, Wikimedia, + CC BY-SA 3.0

    @@ -384,11 +390,12 @@ saying something true or meaningful about whisky is not the task at hand. -
    +
    I like the long notes of smoke and spices in this one. Mmm, gingery. - Photo credit: Pixabay, - CC CC0 1.0 + Photo credit: Pixabay, + CC CC0 1.0

    From 888486b0a5b3ced81a18af483252fbd919abd659 Mon Sep 17 00:00:00 2001 From: Joe Carstairs Date: Fri, 30 Aug 2024 13:22:48 +0100 Subject: [PATCH 19/22] Hides figures from AT with aria-hidden instead of role=presentation --- _posts/2024-08-29-llms-dont-hallucinate.md | 63 ++++++++++------------ 1 file changed, 28 insertions(+), 35 deletions(-) diff --git a/_posts/2024-08-29-llms-dont-hallucinate.md b/_posts/2024-08-29-llms-dont-hallucinate.md index ef6a002b27..12f7695bcd 100644 --- a/_posts/2024-08-29-llms-dont-hallucinate.md +++ b/_posts/2024-08-29-llms-dont-hallucinate.md @@ -15,16 +15,15 @@ events[^7], They can invent legal cases[^8], and fabricate fake science[^9]. These kinds of outputs are often called **hallucinations**.

    -

    +

    @@ -47,16 +46,15 @@ are calling this narrative into question.[^3] **Maybe hallucination isn't a solvable problem at all.**

    -

    +

    @@ -133,15 +131,14 @@ Think about dice. I could roll six sixes in a row, and that would be really unusual behaviour for the die.

    -

    +

    @@ -160,17 +157,16 @@ In contrast, if I secure a picture to the wall with a picture hook, and it falls off, I can reasonably expect there to be an explanation.

    -

    +

    @@ -221,16 +217,15 @@ the most plausible next word in the given sequence. So the case where the LLM 'hallucinates' and the case where it doesn't are causally indistinguishable.

    -

    +

    @@ -308,18 +303,17 @@ organisations are unable to make good decisions or learn from their mistakes[^2].

    -

    +

    @@ -386,16 +380,15 @@ from his Arrans, she just might consider going on a date with me. Actually saying something true or meaningful about whisky is not the task at hand.

    -

    +

    From 772da2739ae65752c25747d9f781ed87f0f69538 Mon Sep 17 00:00:00 2001 From: Joe Carstairs Date: Mon, 2 Sep 2024 17:16:49 +0100 Subject: [PATCH 20/22] 'Lie' -> 'literally lie' --- _posts/2024-08-29-llms-dont-hallucinate.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/_posts/2024-08-29-llms-dont-hallucinate.md b/_posts/2024-08-29-llms-dont-hallucinate.md index 12f7695bcd..3ccf0ebef2 100644 --- a/_posts/2024-08-29-llms-dont-hallucinate.md +++ b/_posts/2024-08-29-llms-dont-hallucinate.md @@ -239,8 +239,8 @@ But there are explanations for why humans sometimes produce such outputs: maybe they were lying, or they were playing a game, or they were joking. Or indeed, perhaps they 'hallucinating' in the traditional sense! -In contrast, an LLM can never lie, can never play a game, and can never tell a -joke. It's a pile of matrices. +In contrast, an LLM can never literally lie, play a game, or tell a joke. It's a +pile of matrices. And there are mechanisms which explain why humans normally don't produce unfaithful or unfactual content. We have mental models of the external world. We From 5e671e1b60875ce915968123061b7ebe10e092ee Mon Sep 17 00:00:00 2001 From: Joe Carstairs Date: Fri, 6 Sep 2024 13:09:16 +0100 Subject: [PATCH 21/22] Updates publish date to 6 Sep --- _posts/2024-09-06-llms-dont-hallucinate.md | 465 +++++++++++++++++++++ 1 file changed, 465 insertions(+) create mode 100644 _posts/2024-09-06-llms-dont-hallucinate.md diff --git a/_posts/2024-09-06-llms-dont-hallucinate.md b/_posts/2024-09-06-llms-dont-hallucinate.md new file mode 100644 index 0000000000..ac869eeee0 --- /dev/null +++ b/_posts/2024-09-06-llms-dont-hallucinate.md @@ -0,0 +1,465 @@ +--- +author: jcarstairs +title: LLMs don't 'hallucinate' +date: 2024-09-06 00:00:00 Z +summary: Describing LLMs as 'hallucinating' fundamentally distorts how LLMs work. We can do better. +category: AI +image: "/jcarstairs/assets/mirage.webp" +layout: default_post +--- + +**LLMs make stuff up.** + +LLMs can confidently tell you all about the winners of non-existent sporting +events[^7], They can invent legal cases[^8], and fabricate fake science[^9]. +These kinds of outputs are often called **hallucinations**. + +

    +

    +

    + +Thankfully, the greatest minds of the human race are hard at work ironing +out this problem. Solutions abound: improving the quality of your training +data[^10], testing the system diligently[^11], fine-tuning the model with human +feedback[^12], integrating external data sources[^13], or even just asking the +LLM to evaluate its own answer[^14] are just some of the ingenious techniques +engineers and scientists have proposed. + +According to OpenAI, +[GPT-4 reduces hallucination by 40%](https://openai.com/index/gpt-4) compared +to its predecessors: and surely, LLM proponents say, that pattern will continue, +Moore's-Law-style, until LLM hallucination is a distant memory, like the +[inefficiencies in early steam engines](https://www.thehistorycorner.org/articles-by-the-team/a-dream-too-lofty-why-the-steam-engine-failed-in-the-18th-century). + +But is it so simple? A growing chorus of academics, engineers and journalists +are calling this narrative into question.[^3] **Maybe hallucination isn't a +solvable problem at all.** + +

    +

    +

    + +Indeed, it's increasingly clear that the word 'hallucination' fundamentally +distorts the way LLMs actually work. This means our judgements of what LLMs are +capable of could be systematically off the mark. + +**We need a new word**. We need a word which captures how LLMs behave, but +doesn't bake in false assumptions about how LLMs work. We need a word which +enables accurate and honest discussions about how best to apply LLMs. + +But before we get to the solution, we need to properly understand the problem. + +## What is 'hallucination'? + +Since about 2017, researchers in natural language processing have used the word +'hallucination' to describe a variety of behaviours[^1]. Since ChatGPT propelled +LLMs to public awareness, this word has become commonplace. But what does it +actually mean? + +Sometimes, it refers to **unfaithfulness**, a jargon word in natural language +processing. This means that its output contains information which is not +supported by the input. + +For example, if you try to get an LLM to summarise a meeting transcript, and its +summary includes events which weren't described in the transcript, the LLM has +been 'unfaithful'. + +When academics talk about 'hallucination', they most often mean something like +'unfaithfulness' – though other meanings are common, too. + +Sometimes, 'hallucination' refers to **non-factuality**: producing outputs which +contain falsehoods, or other falsehood-like things, such as unjustifiable +claims, or claims which contradict the LLM's training data. This is probably the +most common usage in non-academic circles. + +By the way, sometimes, 'hallucination' has another meaning altogether: +**nonsense**. Nonsense is one of those things which is hard to define, but you +know it when you see it. This isn't my focus for this article, so let's leave +nonsense aside for proper treatment another day. + +If you want to go deep on how people are using the word 'hallucination' to talk +about LLMs, I've investigated this in detail on +[my personal blog](https://joeac.net/blog/2024/07/16/word_hallucination_with_reference_to_llms). + +So what people mean by 'hallucination' is pretty diverse. But all of these +different usages have something in common: 'hallucination' is regarded as +**abnormal behaviour**. + +### 'Hallucination' as abnormal behaviour: what this means + +Treating hallucination as abnormal is very significant. Let's take a minute to +consider what this entails. + +First of all, it entails that **every hallucination can be explained**, at least +in principle. + +This is because, when the environment and inputs are normal, systems behave +normally. So, whenever an LLM hallucinates, we should expect to find something +abnormal about the environment or the input which explains why the LLM +hallucinated. + +Secondly, if hallucination is abnormal behaviour, that implies that not +hallucinating is normal. + +That in turn means **there must be some mechanism**, some causal explanation +contained in how LLMs work, which explains why, under normal conditions, LLMs +don't hallucinate. + +These points are pretty subtle, so let's explore a couple of examples. + +Think about dice. I could roll six sixes in a row, and that would be really +unusual behaviour for the die. + +

    +

    +

    + +But firstly, this wouldn't demand an explanation. It's not a bug, it's just +statistically improbable. + +And secondly, there's no mechanism which can explain why, under normal +conditions, a die shouldn't roll six sixes in a row. Very, very occasionally, +it's just what dice do. + +That's because it's normal (albeit uncommon) behaviour for a die to roll six +sixes in a row. + +In contrast, if I secure a picture to the wall with a picture hook, and it +falls off, I can reasonably expect there to be an explanation. + +

    +

    +

    + +Maybe the nail was bent. Maybe I didn't hammer it in straight. Maybe I didn't +quite catch the string on the hook when I put it up. Point is, something must +have gone wrong for the picture hook to behave in this way. + +And again, we should expect there to be a mechanism which can explain why under +normal conditions, pictures hooked to the wall don't fall off. + +And indeed there is such a mechanism: the hook catches the string and transfers +the weight of the picture to the wall, instead of leaving that weight to pull +the picture down towards the ground. + +That's because for the picture to fall off the wall is abnormal behaviour +for a picture hook. + +So, to recap, if LLM hallucination is abnormal behaviour, we should expect two +things: + +1. There should be some abnormal conditions which explain why LLMs hallucinate + in some cases but not others. +2. There should be some mechanism which explains why, under normal conditions, + LLMs don't hallucinate. + +## Why the word 'hallucination' distorts how LLMs work + +**The word 'hallucination' doesn't satisfy either of those two expectations** +listed above. Therefore, the word 'hallucination' fundamentally distorts how +LLMs really work. + +Firstly, when an LLM hallucinates, we should expect there to be some abnormal +conditions which explain why it did so. But often, **there is no such +explanation**. + +Occasionally, LLMs produce unfaithful and untrue content for no good reason. +Often, the problem goes away by making meaningless tweaks in the wording of the +prompt, or even simply by running the algorithm again. + +Secondly, if hallucination is abnormal behaviour, we should expect there to be +some mechanism which causes LLMs not to hallucinate under normal conditions. +But **there is no such mechanism**. + +LLMs are predictive text machines, not oracles. They are not trained to target +faithfulness. They are not trained to target truth. They are trained to guess +the most plausible next word in the given sequence. So the case where the LLM +'hallucinates' and the case where it doesn't are causally indistinguishable. + +

    +

    +

    + +There were some important and difficult points there, so let's elaborate. + +By comparison, for humans to produce false or unfaithful data would indeed be +abnormal behaviour for humans. + +But there are explanations for why humans sometimes produce such outputs: maybe +they were lying, or they were playing a game, or they were joking. Or indeed, +perhaps they 'hallucinating' in the traditional sense! + +In contrast, an LLM can never literally lie, play a game, or tell a joke. It's a +pile of matrices. + +And there are mechanisms which explain why humans normally don't produce +unfaithful or unfactual content. We have mental models of the external world. We +continually amend those models in response to evidence. We receive that evidence +through perception. And we use language, among other things, as a means of +communicating our mental models with other people. + +But an LLM doesn't have any of these mechanisms. It doesn't have a model of +the external world. It doesn't respond to evidence. It doesn't gather evidence +through perception (or, indeed, by any other means). And it doesn't attempt to +use language as a means of communicating information with other minds. + +So the concept of 'hallucination' fundamentally mischaracterises the nature of LLMs +by assuming that false or unfaithful outputs are abnormal for LLMs. In fact, +such outputs, while rare (at least in recent models), are nonetheless normal, +like a dice rolling six sixes in a row. + +## Why this matters + +OK, so 'hallucination' is not a great word. It assumes that unfaithful or +unfactual output is abnormal output for an LLM, when in fact, this just isn't +true. + +But why does this matter? Can't we afford a little fudge? + +I think the use of this word has serious consequences. + +In academia, researchers are spilling considerable amounts of ink trying to +ascertain the causes of 'hallucination'[^5]. + +Not only is this enterprise wholly in vain, but it risks spreading the +misperception that hallucination is incidental, and will disappear once we find +better ways of designing LLMs. That in turn could lead decision-makers to invest +in LLM applications that just won't work, under the false impression that the +'hallucination' problem will disappear in future. + +This isn't a hypothetical concern. LLMs have been set to +[writing entire travel articles](https://arstechnica.com/information-technology/2023/08/microsoft-ai-suggests-food-bank-as-a-cannot-miss-tourist-spot-in-canada), +[making legal submissions](https://www.texaslawblog.law/2023/06/sanctions-handed-down-to-lawyers-who-cited-fake-cases-relying-on-chatgpt), +and [providing customers with information on company policies](https://bc.ctvnews.ca/air-canada-s-chatbot-gave-a-b-c-man-the-wrong-information-now-the-airline-has-to-pay-for-the-mistake-1.6769454) +with catastrophic results. + +And think about some of these common or proposed use cases: + +- Helping write submissions to academic journals +- Generating meeting minutes +- 'Chatting with data' +- Summarising non-structured data in social research +- Writing computer code +- Question-answering +- All-purpose 'AI assistants' + +Given that LLMs will, rarely, but inevitably, output garbage, all of these will +need to be done with enormous care to avoid causing serious damage. + +Furthermore, there's a risk that an LLM may act as a so-called 'moral crumple +zone', effectively soaking up responsibility for failures on behalf of the +humans who were really responsible. And where there's a lack of accountability, +organisations are unable to make good decisions or learn from their +mistakes[^2]. + +

    +

    +

    + + +## How to understand LLM behaviour besides 'hallucinating' + +So, the word 'hallucination' isn't fit for purpose. But we do legitimately need +ways of understanding how LLMs behave, including their tendencies to produce +false or unfaithful outputs. + +### Confabulation: a step forward, but still flawed + +One alternative suggestion is to +[refer to the behaviour as 'confabulation' instead of 'hallucination'](https://journals.plos.org/digitalhealth/article?id=10.1371/journal.pdig.0000388). + +While, as the authors linked above rightly point out, this does address some +of the problems with the word 'hallucination', it still has the fundamental +flaw we've been discussing. It still assumes that 'hallucination', or +'confabulation', is a causally distinguishable abnormal behaviour. + +Humans sometimes confabulate, but normally they don't. When humans do +confabulate, we can expect there to be an explanation, like a head injury or +mental illness. And we can expect there to be mechanisms in how the human mind +works which explains why humans don't normally confabulate. + +But as we have seen, this is not an accurate analogy for LLMs. For an LLM, it is +just as normal for it to produce outputs which are false or unfaithful outputs +as it is for it to produce outputs which are true or faithful. + +### Bulls\*\*t: a better solution + +However, three philosophers at the University of Glasgow have come up with an +ingenious solution. + +[**LLMs are bulls\*\*t machines**](https://link.springer.com/article/10.1007/s10676-024-09775-5). + +Please don't be alarmed. Despite appearances, 'bulls\*\*t' +is a technical term in analytic philosophy, developed in +[Harry Frankfurt's 2005 classic, _On Bulls\*\*t_](https://doi.org/10.2307/j.ctt7t4wr). +Following Frankfurt's original usage, it's generally taken to mean something +like this: + +> **bulls\*\*tting**: speaking with a reckless disregard for the truth. + +Canonical examples include: + +- Pretending you know something about whisky to try and impress a client, + colleague, or crush +- Bizarre advertising slogans, like 'Red Bull gives you wings' +- Any time Paul Merton opens his mouth on [Just A Minute](https://www.bbc.co.uk/programmes/b006s5dp) + +Why is this a good match for describing how LLMs behave? Well, LLMs are trained +to imitate language patterns from its training data. The LLM has succeeded at +its task when its output is linguistically plausible. Not when it's true, +well-evidenced, or faithful to the input – just when it's linguistically +plausible. In other words, its goal is to 'sound like' what a human would say. + +This is just like what a human does when they bulls\*\*t. When someone +bulls\*\*ts, they're not aiming to produce anything true, faithful or coherent, +they're just trying to sound like they know what they're talking about. If I +can convince my crush that I'm a sophisticated gentleman that knows his Islays +from his Arrans, she just might consider going on a date with me. Actually +saying something true or meaningful about whisky is not the task at hand. + +

    +

    +

    + +The word 'bulls\*\*t' is a huge step forward: + +- **It captures LLMs' tendency to produce false or unfaithful output.** Since + truth and faithfulness are not goals, it's perfectly possible for it to + produce false or unfaithful output purely by chance. +- **It explains why LLMs often produce true and faithful output**, even if it is + accidental. Plausible-sounding things, by their nature, have a good chance of + being true. +- **It correctly identifies false and unfaithful outputs as normal for an LLM** + \- unlike 'hallucination'. + +Previously, we thought that an LLM reliably outputs true and faithful content, +except when it occasionally hallucinates. Now, we recognise that an LLM in fact +always outputs bulls\*\*t. + +### The benefits of bulls\*\*t + +So far, the word 'hallucination' has trapped us into talking pretty negatively +about LLMs. But the word 'bulls\*\*t', perhaps surprisingly, frees us to talk +honestly about the positive applications of LLMs as well. + +As several authors have pointed out before[^4], the tendency of LLMs to produce +false and misleading output corresponds closely to its tendency to produce +output which might be described, for want of a better word, as 'creative'. + +Trying to understand why 'hallucinating' goes together with 'creativity' is a +profound mystery. But it's obvious that 'bulls\*\*tting' is in some sense a +'creative' activity by nature. (Just ask Paul Merton!) + +Therefore, because we have a more accurate description of how LLMs work, we +can make more robust decisions both about where LLM behaviour is desirable, and +about where it isn't. + +Would you employ a professional bulls\*\*tter to write your company minutes? Or +write large chunks of computer code? Or summarise a legal contract? + +Or what about redrafting an email in a formal tone? Or suggesting a more +descriptive variable name in your code? Or suggesting a couple extra Acceptance +Criteria on your Scrum Issue that you might have forgotten? Are these +applications where 'sounds about right' is good enough? + +## Recap + +The word 'hallucination' has gripped the popular narrative about LLMs. But the +word was never suited to describing how LLMs behave, and using it puts us at +risk of making bad bets on how LLMs ought to be applied. + +By using the word 'bulls\*\*t', we recognise that while false and unfaithful +outputs may be rare, they should occasionally be expected as part of the normal +operation of the LLM. + +As a result, we are empowered to recognise the applications where LLMs can +really make a positive difference, as well as quickly recognise the applications +where it can't. + +## Footnotes + +[^1]: If you want a fuller treatment of how the word 'hallucination' has been used historically in the field, see [my personal blog post](https://joeac.net/blog/2024/07/16/word_hallucination_with_reference_to_llms) on the topic. +[^2]: For more on this idea, look into [Madeleine Clare Elish's concept of moral crumple zones](https://doi.org/10.17351/ests2019.260). +[^3]: For example, see [a Harvard study on hallucination in the legal context](https://doi.org/10.48550/arXiv.2405.20362), this [philosophical article](https://arxiv.org/pdf/2212.03551) and this [Forbes article](https://www.forbes.com/sites/lanceeliot/2022/08/24/ai-ethics-lucidly-questioning-this-whole-hallucinating-ai-popularized-trend-that-has-got-to-stop) expressing concern about the use of the word 'hallucination', this [BBC journalist on using LLMs in journalism](https://www.bbc.co.uk/rd/blog/2024-06-mitigating-llm-hallucinations-in-text-summarisation), and this [PBS article which quotes an academic saying 'this isn't fixable'](https://www.pbs.org/newshour/science/chatbots-can-make-things-up-can-we-fix-ais-hallucination-problem). +[^4]: See for example [Jiang, Xuhui et al 2024. A Survey on Large Language Model Hallucination via a Creativity Perspective](https://arxiv.org/abs/2402.06647). +[^5]: See for example [Huang, Lei et al 2023. A Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open Questions](https://arxiv.org/abs/2311.05232). +[^7]: Like when [an LLM made up the 1985 World Ski Championships](https://medium.com/@gcentulani/understanding-hallucination-in-llms-causes-consequences-and-mitigation-strategies-b5e1d0268069). +[^8]: As one person found to their misfortune, when [a New York lawyer filed a brief containing six references to non-existent cases](https://www.bbc.co.uk/news/world-us-canada-65735769). +[^9]: [Meta's AI academic assistant Galactica flopped after three days](https://www.technologyreview.com/2022/11/18/1063487/meta-large-language-model-ai-only-survived-three-days-gpt-3-science), following a backlash over its tendency to output falsehoods. +[^10]: See for example [Gianluca Centulani on Medium](https://medium.com/@gcentulani/understanding-hallucination-in-llms-causes-consequences-and-mitigation-strategies-b5e1d0268069). +[^11]: See for example [IBM's introduction to LLM hallucination](https://www.ibm.com/topics/ai-hallucinations) +[^12]: As claimed by [OpenAI's GPT-4](https://openai.com/index/gpt-4). +[^13]: See for example [Shuster et al 2021. Retrieval Augmentation Reduces Hallucination in Conversation](https://arxiv.org/pdf/2104.07567). +[^14]: See for example [Ji et al 2024. Towards Mitigating Hallucination in Large Language Models via Self-Reflection](https://aclanthology.org/2023.findings-emnlp.123.pdf). From 139e0cc131472176812d3cd19991b8e71926c10c Mon Sep 17 00:00:00 2001 From: Joe Carstairs Date: Tue, 10 Sep 2024 14:43:28 +0100 Subject: [PATCH 22/22] Bumps date to 10th Sept --- ...-dont-hallucinate.md => 2024-09-10-llms-dont-hallucinate.md} | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) rename _posts/{2024-09-06-llms-dont-hallucinate.md => 2024-09-10-llms-dont-hallucinate.md} (99%) diff --git a/_posts/2024-09-06-llms-dont-hallucinate.md b/_posts/2024-09-10-llms-dont-hallucinate.md similarity index 99% rename from _posts/2024-09-06-llms-dont-hallucinate.md rename to _posts/2024-09-10-llms-dont-hallucinate.md index ac869eeee0..efc495d286 100644 --- a/_posts/2024-09-06-llms-dont-hallucinate.md +++ b/_posts/2024-09-10-llms-dont-hallucinate.md @@ -1,7 +1,7 @@ --- author: jcarstairs title: LLMs don't 'hallucinate' -date: 2024-09-06 00:00:00 Z +date: 2024-09-10 00:00:00 Z summary: Describing LLMs as 'hallucinating' fundamentally distorts how LLMs work. We can do better. category: AI image: "/jcarstairs/assets/mirage.webp"