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title: README
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Lumos: Learning Agents with Unified Data, Modular Design, and Open-Source LLMs
Website: https://allenai.github.io/lumos/
Paper:
Github: https://github.com/allenai/lumos
🪄 Lumos: Learning Agents with Unified Data, Modular Design, and Open-Source LLMs
🖋 Authors: Da Yin, Faeze Brahman, Abhilasha Ravichander, Khyathi Chandu, Kai-Wei Chang, Yejin Choi, Bill Yuchen Lin
We introduce 🪄Lumos, Language Agents with Unified Data Formats, Modular Design, and Open-Source LLMs. Lumos unifies a suite of complex interactive tasks and achieves competitive performance with GPT-4/3.5-based and larger open-source agents.
‼️ Lumos has following features:
- 🧩 Modular Architecture:
- Lumos consists of planning, grounding, and execution modules built based on LLAMA-2-7B.
- 🌍 Diverse Training Data:
- Lumos is trained with ~40K high-quality annotations from ground-truth reasoning steps in existing benchmarks with GPT-4.
- 🚀 Competitive Performance:
- 🚀 Lumos outperforms GPT-4/3.5-based agents on complex QA and web tasks, and larger open agents on maths tasks.
- 🚀 Lumos performs better than open agent baseline formulations including chain-of-thoughts and integrated training.
- 🚀 Lumos surpasses larger open LLM agents and domain-specific agents on an unseen task, WebShop.
🤩 Citation
If you find this work is relevant with your research, please feel free to cite our work!
@article{yin2023lumos,
title={Lumos: Language Agents with Unified Data Formats, Modular Design, and Open-Source LLMs},
author={Yin, Da and Brahman, Faeze and Ravichander, Abhilasha and Chandu, Khyathi and Chang, Kai-Wei and Choi, Yejin and Lin, Bill Yuchen},
year={2023}
}