title: Lumos: Learning Agents with Unified Data, Modular Design, and
    Open-Source LLMs
summary: We introduce Lumos, a novel framework for training language agents that
  employs a unified data format and a modular architecture based on open-source
  large language models (LLMs). Lumos consists of three distinct modules:
  planning, grounding, and execution. The planning module breaks down a task into
  a series of high-level, tool-agnostic subgoals, which are then made specific by
  the grounding module through a set of low-level actions. These actions are
  subsequently executed by the execution module, utilizing a range of
  off-the-shelf tools and APIs. In order to train these modules effectively,
  high-quality annotations of subgoals and actions were collected and are made
  available for fine-tuning open-source LLMs for various tasks such as complex
  question answering, web tasks, and math problems. Leveraging this unified data
  and modular design, Lumos not only achieves comparable or superior performance
  to current, state-of-the-art agents, but also exhibits several key advantages:
  (1) Lumos surpasses GPT-4/3.5-based agents in complex question answering and
  web tasks, while equalling the performance of significantly larger LLM agents
  on math tasks; (2) Lumos outperforms open-source agents created through
  conventional training methods and those using chain-of-thoughts training; and
  (3) Lumos is capable of effectively generalizing to unseen interactive tasks,
  outperforming larger LLM-based agents and even exceeding performance of
  specialized agents.
paper_authors._id: 65518d2efd13d5b5ec9f1204
paper_authors.name: Da Yin
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paper_authors.name: Abhilasha Ravichander
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paper_authors.fullname: Abhilasha Ravichander
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paper_authors.user: lasha-nlp
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paper_authors.name: Khyathi Chandu
paper_authors._id: 65518d2efd13d5b5ec9f1208
paper_authors.name: Kai-Wei Chang
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paper_authors.fullname: Kai-Wei Chang
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paper_authors.user: kaiweichang
paper_authors._id: 65518d2efd13d5b5ec9f1209
paper_authors.name: Yejin Choi
paper_authors.status: admin_assigned
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paper_authors.avatarUrl: /avatars/52e54bdd6a1fb6c774a40cd70f3d7925.svg
paper_authors.fullname: Yejin Choi
paper_authors.type: user
paper_authors.user: yejinchoinka
paper_authors._id: 65518d2efd13d5b5ec9f120a
paper_authors.name: Bill Yuchen Lin
paper_authors.status: claimed_verified
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paper_authors.fullname: Bill Yuchen Lin
paper_authors.type: user
paper_authors.user: yuchenlin
paper_title: Lumos: Learning Agents with Unified Data, Modular Design, and
    Open-Source LLMs
paper_summary: We introduce Lumos, a novel framework for training language agents that
  employs a unified data format and a modular architecture based on open-source
  large language models (LLMs). Lumos consists of three distinct modules:
  planning, grounding, and execution. The planning module breaks down a task into
  a series of high-level, tool-agnostic subgoals, which are then made specific by
  the grounding module through a set of low-level actions. These actions are
  subsequently executed by the execution module, utilizing a range of
  off-the-shelf tools and APIs. In order to train these modules effectively,
  high-quality annotations of subgoals and actions were collected and are made
  available for fine-tuning open-source LLMs for various tasks such as complex
  question answering, web tasks, and math problems. Leveraging this unified data
  and modular design, Lumos not only achieves comparable or superior performance
  to current, state-of-the-art agents, but also exhibits several key advantages:
  (1) Lumos surpasses GPT-4/3.5-based agents in complex question answering and
  web tasks, while equalling the performance of significantly larger LLM agents
  on math tasks; (2) Lumos outperforms open-source agents created through
  conventional training methods and those using chain-of-thoughts training; and
  (3) Lumos is capable of effectively generalizing to unseen interactive tasks,
  outperforming larger LLM-based agents and even exceeding performance of
  specialized agents.
paper_ai_keywords: language agents planning module tool-agnostic subgoals grounding module low-level actions execution module off-the-shelf tools APIs fine-tuning open-source LLMs complex question answering web tasks math problems state-of-the-art agents chain-of-thoughts training generalizing to unseen interactive tasks specialized agents