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<!--# LMOps: Enabling AI w/ LLMs-->

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.

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Prompt Intelligence

Advanced technologies facilitating prompting language models.

Promptist: reinforcement learning for automatic prompt optimization

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

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

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

  1. Prepend (many) retrieved (long) documents as context in GPT.
  1. Scale in-context learning to many demonstration examples.

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X-Prompt: extensible prompts beyond NL for descriptive instructions

[Paper] Extensible Prompts for Language Models

Extensible Prompts for Language Models

LLMA: LLM Accelerators

Accelerate LLM Inference with References

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

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Fundamental Understanding of LLMs

Understanding In-Context Learning

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

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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 <a href="mailto:fuwei@microsoft.com" class="x-hidden-focus">fuwei@microsoft.com</a>.

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 (fuwei@microsoft.com).