category
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: Automatic Prompt Optimization, Promptist, Extensible prompts, Universal prompt retrieval, LLM Retriever, In-Context Demonstration Selection
- Longer Context: Structured prompting, Length-Extrapolatable Transformers
- LLM Alignment: Alignment via LLM feedback
- LLM Accelerator (Faster Inference): Lossless Acceleration of LLMs
- LLM Customization: Adapt LLM to domains
- 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] Nov, 2023: In-Context Demonstration Selection with Cross Entropy Difference (EMNLP 2023)
- [Paper Release] Oct, 2023: Tuna: Instruction Tuning using Feedback from Large Language Models (EMNLP 2023)
- [Paper Release] Oct, 2023: Automatic Prompt Optimization with "Gradient Descent" and Beam Search (EMNLP 2023)
- [Paper Release] Oct, 2023: UPRISE: Universal Prompt Retrieval for Improving Zero-Shot Evaluation (EMNLP 2023)
- [Paper Release] July, 2023: Learning to Retrieve In-Context Examples for Large Language Models
- [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:
- Prepend (many) retrieved (long) documents as context in GPT.
- 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/GeneralAI
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 fuwei@microsoft.com.
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