AgentTuning: Enabling Generalized Agent Abilities for LLMs
Open large language models (LLMs) with great performance in various tasks
have significantly advanced the development of LLMs. However, they are far
inferior to commercial models such as ChatGPT and GPT-4 when acting as agents
to tackle complex tasks in the real world. These agent tasks employ LLMs as the
central controller responsible for planning, memorization, and tool
utilization, necessitating both fine-grained prompting methods and robust LLMs
to achieve satisfactory performance. Though many prompting methods have been
proposed to complete particular agent tasks, there is lack of research focusing
on improving the agent capabilities of LLMs themselves without compromising
their general abilities. In this work, we present AgentTuning, a simple and
general method to enhance the agent abilities of LLMs while maintaining their
general LLM capabilities. We construct AgentInstruct, a lightweight
instruction-tuning dataset containing high-quality interaction trajectories. We
employ a hybrid instruction-tuning strategy by combining AgentInstruct with
open-source instructions from general domains. AgentTuning is used to
instruction-tune the Llama 2 series, resulting in AgentLM. Our evaluations show
that AgentTuning enables LLMs' agent capabilities without compromising general
abilities. The AgentLM-70B is comparable to GPT-3.5-turbo on unseen agent
tasks, demonstrating generalized agent capabilities. We open source the
AgentInstruct and AgentLM-7B, 13B, and 70B models at
https://github.com/THUDM/AgentTuning , serving open and powerful alternatives
to commercial LLMs for agent tasks.
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Aohan Zeng
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2023-10-20T08:05:59.721Z
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Zeng Aohan
user
Sengxian
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Mingdao Liu
admin_assigned
2023-10-20T08:24:04.545Z
64f1abd2b12bdcef55e1c078
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Mingdao Liu
user
lambdax
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Rui Lu
6531dc81e7897259333f12d4
Bowen Wang
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Xiao Liu
admin_assigned
2023-10-20T08:28:18.151Z
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Xiao Liu
user
ShawLiu
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Yuxiao Dong
admin_assigned
2023-10-20T08:25:37.715Z
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Yuxiao Dong
user
yuxiaod
6531dc81e7897259333f12d7
Jie Tang
admin_assigned
2023-10-20T08:25:53.463Z
640dff05474aa6f89556677e
/avatars/1b4591c7322d649c797b3125148f1915.svg
Jie Tang
user
jerytang
AgentTuning: Enabling Generalized Agent Abilities for LLMs
Open large language models (LLMs) with great performance in various tasks
have significantly advanced the development of LLMs. However, they are far
inferior to commercial models such as ChatGPT and GPT-4 when acting as agents
to tackle complex tasks in the real world. These agent tasks employ LLMs as the
central controller responsible for planning, memorization, and tool
utilization, necessitating both fine-grained prompting methods and robust LLMs
to achieve satisfactory performance. Though many prompting methods have been
proposed to complete particular agent tasks, there is lack of research focusing
on improving the agent capabilities of LLMs themselves without compromising
their general abilities. In this work, we present AgentTuning, a simple and
general method to enhance the agent abilities of LLMs while maintaining their
general LLM capabilities. We construct AgentInstruct, a lightweight
instruction-tuning dataset containing high-quality interaction trajectories. We
employ a hybrid instruction-tuning strategy by combining AgentInstruct with
open-source instructions from general domains. AgentTuning is used to
instruction-tune the Llama 2 series, resulting in AgentLM. Our evaluations show
that AgentTuning enables LLMs' agent capabilities without compromising general
abilities. The AgentLM-70B is comparable to GPT-3.5-turbo on unseen agent
tasks, demonstrating generalized agent capabilities. We open source the
AgentInstruct and AgentLM-7B, 13B, and 70B models at
https://github.com/THUDM/AgentTuning , serving open and powerful alternatives
to commercial LLMs for agent tasks.
AgentTuning enhances the agent capabilities of open LLMs through instruction-tuning without compromising their general abilities, demonstrating performance comparable to commercial models on unseen tasks.
LLMs AgentTuning AgentInstruct instruction-tuning AgentLM