🪄 Lumos: Language Agents with Unified Formats, Modular Design, and Open-Source LLMs
🌐[Website] 📝[Paper] 🤗[Data] 🤗[Model]
We introduce 🪄Lumos, Language Agents with Unified 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 agent tasks, and larger open agents on maths tasks.
- 🚀 Lumos performs better than open agent baseline formulations including chain-of-thoughts and unmodularized training.
- 🚀 Lumos surpasses larger open LLM agents and domain-specific agents on an unseen task, WebShop.
Model Overview
lumos_maths_plan_iterative is a planning module checkpoint finetuned on maths task in Lumos-Iterative (Lumos-I) formulation.
The training annotation is shown below:
| Training Data | Number |
|---|---|
lumos_maths_plan_iterative |
19778 |
Citation
If you find this work is relevant with your research, please feel free to cite our work!
@article{yin2023lumos,
title={Lumos: Towards Language Agents that are Unified, Modular, and Open Source},
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}
}