LMOps

LMOps is a research initiative on fundamental research and technology for building AI products w/ foundation models, especially on the general technology for enabling AI capabilities w/ LLMs and Generative AI models.

• Better Prompts: Promptist, Extensible prompts, Universal prompt retrieval
• Longer Context: Structured prompting, Length-Extrapolatable Transformers
• LLM Accelerator (Faster Inference): Lossless Acceleration of LLMs
• LLM Customization (TBA)
• Fundamentals: Understanding In-Context Learning

Links

• microsoft/unilm: Large-scale Self-supervised Pre-training Across Tasks, Languages, and Modalities
• microsoft/torchscale: Transformers at (any) Scale

News

• [Paper Release] April, 2023: Inference with Reference: Lossless Acceleration of Large Language Models
• [Paper Release] Dec, 2022: Why Can GPT Learn In-Context? Language Models Secretly Perform Finetuning as Meta Optimizers
• [Paper & Model & Demo Release] Dec, 2022: Optimizing Prompts for Text-to-Image Generation
• [Paper & Code Release] Dec, 2022: Structured Prompting: Scaling In-Context Learning to 1,000 Examples
• [Paper Release] Nov, 2022: Extensible Prompts for Language Models

Prompt Intelligence

Advanced technologies facilitating prompting language models.

Promptist: reinforcement learning for automatic prompt optimization

[Paper] Optimizing Prompts for Text-to-Image Generation

• Language models serve as a prompt interface that optimizes user input into model-preferred prompts.

• Learn a language model for automatic prompt optimization via reinforcement learning.

Structured Prompting: consume long-sequence prompts in an efficient way

[Paper] Structured Prompting: Scaling In-Context Learning to 1,000 Examples

• Example use cases:

1. Prepend (many) retrieved (long) documents as context in GPT.

2. Scale in-context learning to many demonstration examples.

X-Prompt: extensible prompts beyond NL for descriptive instructions

[Paper] Extensible Prompts for Language Models

• Extensible interface allowing prompting LLMs beyond natural language for fine-grain specifications

• Context-guided imaginary word learning for general usability

LLMA: LLM Accelerators

Accelerate LLM Inference with References

[Paper] Inference with Reference: Lossless Acceleration of Large Language Models

• Outputs of LLMs often have significant overlaps with some references (e.g., retrieved documents).

• LLMA losslessly accelerate the inference of LLMs by copying and verifying text spans from references into the LLM inputs.

• Applicable to important LLM scenarios such as retrieval-augmented generation and multi-turn conversations.

• Achieves 2~3 times speed-up without additional models.

Fundamental Understanding of LLMs

Understanding In-Context Learning

[Paper] Why Can GPT Learn In-Context? Language Models Secretly Perform Finetuning as Meta Optimizers

• According to the demonstration examples, GPT produces meta gradients for In-Context Learning (ICL) through forward computation. ICL works by applying these meta gradients to the model through attention.

• The meta optimization process of ICL shares a dual view with finetuning that explicitly updates the model parameters with back-propagated gradients.

• We can translate optimization algorithms (such as SGD with Momentum) to their corresponding Transformer architectures.

Hiring: aka.ms/nlpagi

We are hiring at all levels (including FTE researchers and interns)! If you are interested in working with us on Foundation Models (aka large-scale pre-trained models) and AGI, NLP, MT, Speech, Document AI and Multimodal AI, please send your resume to [email protected].

License

This project is licensed under the license found in the LICENSE file in the root directory of this source tree.

Microsoft Open Source Code of Conduct

Contact Information

For help or issues using the pre-trained models, please submit a GitHub issue. For other communications, please contact Furu Wei ([email protected]).