Lumos: Learning Agents with Unified Data, Modular Design, and
  Open-Source LLMs
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.
65518d2efd13d5b5ec9f1204
Da Yin
claimed_verified
2023-11-13T08:49:37.015Z
634e4670a51d5df8c2d92fce
/avatars/c52d7150b4de6a2eb2d83b345d35cbc2.svg
Da Yin
user
DaYin
65518d2efd13d5b5ec9f1205
Faeze Brahman
admin_assigned
2023-11-13T08:42:38.020Z
65282b8d578679aac7888aec
https://cdn-avatars.huggingface.co/v1/production/uploads/65282b8d578679aac7888aec/dibBkhH-z1c70mJZZxJ7u.jpeg
Faeze Brahman
user
faezeb
65518d2efd13d5b5ec9f1206
Abhilasha Ravichander
admin_assigned
2023-11-13T08:42:45.888Z
6349886c429608888c42319a
/avatars/f84b5fe8b76172878274754e3399d6ec.svg
Abhilasha Ravichander
user
lasha-nlp
65518d2efd13d5b5ec9f1207
Khyathi Chandu
65518d2efd13d5b5ec9f1208
Kai-Wei Chang
admin_assigned
2023-11-13T08:43:27.324Z
60b7b9d71b90c5d07c23fbd0
https://cdn-avatars.huggingface.co/v1/production/uploads/1622653364258-noauth.jpeg
Kai-Wei Chang
user
kaiweichang
65518d2efd13d5b5ec9f1209
Yejin Choi
admin_assigned
2023-11-13T08:43:35.645Z
64d42729f63b01b7f676b176
/avatars/52e54bdd6a1fb6c774a40cd70f3d7925.svg
Yejin Choi
user
yejinchoinka
65518d2efd13d5b5ec9f120a
Bill Yuchen Lin
claimed_verified
2023-11-13T08:31:31.883Z
607f666a4ad99100d63ce35c
https://cdn-avatars.huggingface.co/v1/production/uploads/607f666a4ad99100d63ce35c/QxhxnvfeV6efkxwUFHwjI.png
Bill Yuchen Lin
user
yuchenlin
Lumos: Learning Agents with Unified Data, Modular Design, and
  Open-Source LLMs
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.
Lumos, a modular language agent framework using open-source LLMs, achieves superior performance across various tasks and demonstrates enhanced generalization.
language agents open-source large language models LLMs planning module grounding module execution module subgoals low-level actions fine-tuning complex question answering web tasks math problems chain-of-thoughts training interactive tasks specialized agents