metadata
license: apache-2.0
datasets:
- ai2lumos/lumos_complex_qa_ground_iterative
language:
- en
tags:
- language-agent
- question-answering
- reasoning
- grounding
馃獎 Agent Lumos: Unified and Modular Training for Open-Source Language Agents
馃寪[Website] 馃摑[Paper] 馃[Data] 馃[Model] 馃[Demo]
We introduce 馃獎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:
- 馃З Modular Architecture:
- 馃З Lumos consists of planning, grounding, and execution modules built based on LLAMA-2-7B/13B and off-the-shelf APIs.
- 馃 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.
- 馃實 Diverse Training Data:
- 馃實 Lumos is trained with ~56K diverse high-quality subgoal/action annotations from ground-truth reasoning steps in existing benchmarks with GPT-4.
- 鈿掞笍 Lumos data can be instrumental for future research in developing open-source agents for complex interactive tasks.
- 馃殌 Competitive Performance:
- 馃殌 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.
- 馃殌 Lumos exceeds contemporaneous agents that have been fine-tuned with in-domain HotpotQA, Mind2Web and ScienceQA annotations, such as FiReAct, AgentLM, and AutoAct.
- 馃殌 Lumos performs better than open agent baseline formulations including chain-of-thoughts and integrated training.
- 馃殌 Lumos surpasses larger open LLM agents and domain-specific agents on unseen tasks, WebShop and InterCode_SQL.
Model Overview
lumos_complex_qa_ground_iterative is a grounding module checkpoint finetuned on complex QA task in Lumos-Iterative (Lumos-I) formulation.
The training annotation is shown below:
| Training Data | Number |
|---|---|
lumos_complex_qa_ground_iterative |
19409 |
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}
}