LUMOS: Towards Language Agents that are Unified, Modular, and Open Source

Da Yin, Faeze Brahman, Abhilasha Ravichander, Khyathi Chandu, Kai-Wei Chang, Yejin Choi, Bill Yuchen Lin

Primary Area: representation learning for computer vision, audio, language, and other modalities

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Keywords: language agent, interactive NLP, tool-augmented LLM

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Abstract: In this paper, we present LUMOS, **L**anguage agents with **U**nified formats, **M**odular design, and **O**pen **S**ource LLMs. LUMOS features a modular architecture consisting of planning, grounding, and execution modules built based on open-source LLMs such as LLAMA-2. The planning module decomposes a task into a sequence of high-level subgoals; the grounding module then grounds the generated subgoals to a series of low-level actions that can then be executed by the execution module. To obtain high-quality annotations for training these modules, we leverage LLMs to convert ground-truth intermediate reasoning steps in existing benchmarks into a unified format that can be used in the LUMOS framework. LUMOS achieves competitive or superior performance compared to the state of the art on a variety of complex interactive tasks. We observe: (1) LUMOS is competitive with the LLM agents that are 2 − 4× larger on maths tasks, and outperforms GPT-4/3.5-based agents on complex QA and web agent tasks; (2) LUMOS shows superior performance against open-source agent baseline formulations including chain-of-thoughts fine-tuning and unmodularized training; (3) LUMOS surpasses larger LLM-based agents on an unseen interactive task, WebShop, and achieves 5-10 reward improvement over domain-specific agents.

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Submission Number: 8483