LMOps: Enabling AI with Large Language Models
LMOps is a research initiative focused on the foundational research and technology required to build AI products using foundation models. This includes exploring general technology that enables AI capabilities with Large Language Models (LLMs) and Generative AI models.
Key Focus Areas
1. Better Prompts:
LMOps delves into optimizing prompts for LLMs to improve interactions:
- Automatic Prompt Optimization - Techniques for making LLM prompts more effective.
- Promptist - An approach that learns to optimize prompts using reinforcement learning.
- Extensible Prompts and Universal Prompt Retrieval - Allows for more flexible and efficient prompt usage.
- LLM Retriever and In-Context Demonstration Selection - Methods for selecting contextually relevant examples to enhance LLM performance.
2. Longer Context Handling:
Effective ways to handle long-sequence prompts using:
- Structured Prompting - Allows LLMs to leverage extensive context efficiently.
- Length-Extrapolatable Transformers - Models designed to handle longer input sequences effectively.
3. LLM Alignment:
Research focuses on aligning LLMs with desired outputs using feedback mechanisms.
4. LLM Accelerator (Faster Inference):
- Lossless Acceleration - Improves the speed of LLM inference without compromising on the quality, utilizing references to enhance performance.
5. LLM Customization:
Adaptation of LLMs to specific domains or specialized tasks, enabling them to function more effectively within particular contexts.
6. Fundamental Understanding of LLMs:
- Understanding In-Context Learning - Investigates how LLMs can implicitly fine-tune via in-context examples to generate better responses.
Tools and Resources
GitHub Resources:
- Microsoft/unilm: This repository focuses on large-scale self-supervised pre-training across various tasks, languages, and modalities.
- Microsoft/torchscale: Offers scalable transformer models suitable for various AI tasks.
Recent Developments and Publications
LMOps has made several breakthroughs as evidenced by recent papers and releases:
- In-Context Demonstration Selection (Nov 2023)
- Instruction Tuning using Feedback from Large Language Models (Oct 2023)
- Automatic Prompt Optimization (Oct 2023)
- Universal Prompt Retrieval for Zero-Shot Evaluation (Oct 2023)
- And more, demonstrating the continual advancement of techniques and technologies in AI.
Fundamental Understanding
Understanding In-Context Learning:
Explores the secret processes by which GPT-like models perform in-context learning, equating this with meta optimization and finetuning methodologies.
Hiring Opportunities
The LMOps team is actively recruiting at all levels for researchers and interns. They invite participation from those interested in foundational models, NLP, AGI, and more. Potential applicants can contact the team via email for more information.
Licensing and Conduct
The LMOps project adheres to the licensing terms specified in their LICENSE file, guided by Microsoft's Open Source Code of Conduct principles.
For further details or to address any issues regarding the models, users are encouraged to engage via GitHub or contact Furu Wei directly for more-specific inquiries regarding the project.