metadata

license: apache-2.0
datasets:
  - ai2lumos/lumos_maths_plan_onetime
language:
  - en
tags:
  - language-agent
  - maths
  - reasoning
  - planning

🪄 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_onetime is a planning module checkpoint finetuned on maths task in Lumos-Onetime (Lumos-O) formulation.

The training annotation is shown below:

Training Data	Number
`lumos_maths_plan_onetime`	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}
}