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

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

• [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

Advanced technologies facilitating prompting language models.

[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.