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Human Feedback is not Gold Standard

Tom Hosking, Phil Blunsom, Max Bartolo

Description

Human feedback has become the de facto standard for evaluating the performance of Large Language Models, and is increasingly being used as a training objective. However, it is not clear which properties of a generated output this single `preference' score captures. We hypothesise that preference scores are subjective and open to undesirable biases. We critically analyse the use of human feedback for both training and evaluation, to verify whether it fully captures a range of crucial error criteria. We find that while preference scores have fairly good coverage, they under-represent important aspects like factuality. We further hypothesise that both preference scores and error annotation may be affected by confounders, and leverage instruction-tuned models to generate outputs that vary along two possible confounding dimensions: assertiveness and complexity. We find that the assertiveness of an output skews the perceived rate of factuality errors, indicating that human annotations are not a fully reliable evaluation metric or training objective. Finally, we offer preliminary evidence that using human feedback as a training objective disproportionately increases the assertiveness of model outputs. We encourage future work to carefully consider whether preference scores are well aligned with the desired objective.

Repo Overview

./interface contains our modified version of Potato and the configs used for annotation.

./notebooks contains the notebooks used to sample from the models, filter the datasets, and run the analysis: DataPreparation.ipynb for data preprocessing AnalysisPart1.ipynb for analysis and plot generation for the first set of experiments AnalysisPart2.ipynb for analysis and plot generation for the second set of experiments

./results contains the annotated datasets.

Datasets

For Part 1, the continuations from output_merged_{system}.jsonl were used to generate annotation batches.

The unaggregated ratings are available in the following files:

./results/batch_v1/prolific_results/unbiased_full.parquet
./results/batch_v1/prolific_results/granular_full.parquet

The aggregrated ratings with distractors removed are in ./results/batch_v1/prolific_results/combined_filtered.parquet

Part 1 data also contains subjective scores, which were unused: Detail, Clarity, Creativity, Usefuless, Style and Confidence

For Part 2, the continuations in augmented_v2_{system}.jsonl + augmented_v2_part2_{system}.jsonl were used, and combined into output_augmented_merged.jsonl, which was used to generate the annotation batches.

The unaggregated and aggregated+filtered annotations are in ./results/batch_controlled/prolific_results/.

The prolific_* files are the batches used as input to Potato. The prolific_results/annotated_instances_* files are the raw output from Potato.

API keys for inference

For generating completions, API keys should be available as environment variables, eg:

HF_HUB_KEY=hf_????
COHERE_API_KEY=???

Annotation interface

The relevant interface configs are: For Part 1: granular-eval-detailed for errors granular-eval-unbiased for unbiased quality scores

Part 2: confound-errors for errors confound-grouping for assertiveness/complexity ratings confound-unbiased for unbiased quality scores granular-eval-detailed-verification for expert annotations

Start Potato with a command like python potato/potato/flask_server.py start confound-grouping/confound-grouping.yaml -p 15000

Citation

@inproceedings{
      hosking2024human,
      title={Human Feedback is not Gold Standard},
      author={Tom Hosking and Phil Blunsom and Max Bartolo},
      booktitle={The Twelfth International Conference on Learning Representations},
      year={2024},
      url={https://openreview.net/forum?id=7W3GLNImfS}
}