[R] ReWOO: Decoupling Reasoning from Observations for Efficient Augmented Language Models - Binfeng Xu et al Microsoft 2023 - Achieves 5x token efficiency and 4% accuracy improvement on HotpotQA!

Paper: [https://arxiv.org/abs/2305.18323](https://arxiv.org/abs/2305.18323)

Github: [https://github.com/billxbf/ReWOO](https://github.com/billxbf/ReWOO)

Twitter: [https://twitter.com/billxbf/status/1663713374910251009?s=20](https://twitter.com/billxbf/status/1663713374910251009?s=20)

Hugging Face Demo: [https://huggingface.co/spaces/rewoo/ReWOO-Demo](https://huggingface.co/spaces/rewoo/ReWOO-Demo)

Abstract:

>Augmented Language Models (ALMs) blend the reasoning capabilities of Large Language Models (LLMs) with tools that allow for knowledge retrieval and action execution. Existing ALM systems trigger LLM thought processes while pulling observations from these tools in an interleaved fashion. Specifically, an LLM reasons to call an external tool, gets halted to fetch the tool's response, and then decides the next action based on all preceding response tokens. Such …

abstract call fashion fetch knowledge language language models large language models llm llms machinelearning processes reasoning retrieval systems thought tool tools