metadata

license: apache-2.0
datasets:
  - ai2lumos/lumos_multimodal_plan_iterative
language:
  - en
tags:
  - language-agent
  - visual-question-answering
  - reasoning
  - planning

🪄 Agent Lumos: Unified and Modular Training for Open-Source Language Agents

🌐[Website] 📝[Paper] 🤗[Data] 🤗[Model] 🤗[Demo]

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/13B and off-the-shelf APIs.
- 🤗 Lumos utilizes a unified data format that encompasses multiple task types, thereby enabling the developed agent framework to conveniently support a range of interactive tasks.
🌍 Diverse Training Data:
- 🌍 Lumos is trained with ~56K diverse high-quality subgoal/action annotations from ground-truth reasoning steps in existing benchmarks with GPT-4.
- ⚒️ Lumos data can be instrumental for future research in developing open-source agents for complex interactive tasks.
🚀 Competitive Performance:
- 🚀 Lumos is comparable or even beats GPT-series agents on web/complex QA tasks Mind2Web and HotpotQA, and larger open agents on math and multimodal tasks.
- 🚀 Lumos exceeds contemporaneous agents that have been fine-tuned with in-domain HotpotQA, Mind2Web and ScienceQA annotations, such as FiReAct, AgentLM, and AutoAct.
- 🚀 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 unseen tasks, WebShop and InterCode_SQL.

Model Overview

lumos_multimodal_plan_iterative-13B is a planning module checkpoint finetuned on multimodal task in Lumos-Iterative (Lumos-I) formulation.

The training annotation is shown below:

Training Data	Number
`lumos_multimodal_plan_iterative`	19541

Citation

If you find this work is relevant with your research, please feel free to cite our work!

@article{yin2023lumos,
  title={Agent Lumos: Unified and Modular Training for Open-Source Language Agents},
  author={Yin, Da and Brahman, Faeze and Ravichander, Abhilasha and Chandu, Khyathi and Chang, Kai-Wei and Choi, Yejin and Lin, Bill Yuchen},
  journal={arXiv preprint arXiv:2311.05657},
  year={2023}
}