Lumos: Learning Agents with Unified Data, Modular Design, and
  Open-Source LLMs
Authors: name: Da Yin, name: Faeze Brahman, name: Abhilasha Ravichander, name: Khyathi Chandu, name: Kai-Wei Chang, name: Yejin Choi, name: Bill Yuchen Lin

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.