Agent-FLAN: Designing Data and Methods of Effective Agent Tuning for
  Large Language Models
Open-sourced Large Language Models (LLMs) have achieved great success in
various NLP tasks, however, they are still far inferior to API-based models
when acting as agents. How to integrate agent ability into general LLMs becomes
a crucial and urgent problem. This paper first delivers three key observations:
(1) the current agent training corpus is entangled with both formats following
and agent reasoning, which significantly shifts from the distribution of its
pre-training data; (2) LLMs exhibit different learning speeds on the
capabilities required by agent tasks; and (3) current approaches have
side-effects when improving agent abilities by introducing hallucinations.
Based on the above findings, we propose Agent-FLAN to effectively Fine-tune
LANguage models for Agents. Through careful decomposition and redesign of the
training corpus, Agent-FLAN enables Llama2-7B to outperform prior best works by
3.5\% across various agent evaluation datasets. With comprehensively
constructed negative samples, Agent-FLAN greatly alleviates the hallucination
issues based on our established evaluation benchmark. Besides, it consistently
improves the agent capability of LLMs when scaling model sizes while slightly
enhancing the general capability of LLMs. The code will be available at
https://github.com/InternLM/Agent-FLAN.
65fa6243180d06d37c8528cf
Zehui Chen
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2024-03-21T12:59:52.477Z
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Zehui Chen
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Kuikun Liu
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2024-08-02T07:35:17.072Z
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liu
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Harold-lkk
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Qiuchen Wang
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2025-02-28T12:18:21.417Z
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QiuchenWang
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autumncc
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Wenwei Zhang
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2024-03-21T13:00:20.722Z
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Wenwei Zhang
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ZwwWayne
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Jiangning Liu
admin_assigned
2024-03-21T13:00:26.955Z
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Jiangning Liu
user
liujiangning
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Dahua Lin
admin_assigned
2024-03-21T13:00:33.766Z
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Dahua Lin
user
lindahua
65fa6243180d06d37c8528d5
Kai Chen
65fa6243180d06d37c8528d6
Feng Zhao
Agent-FLAN: Designing Data and Methods of Effective Agent Tuning for
  Large Language Models
Open-sourced Large Language Models (LLMs) have achieved great success in
various NLP tasks, however, they are still far inferior to API-based models
when acting as agents. How to integrate agent ability into general LLMs becomes
a crucial and urgent problem. This paper first delivers three key observations:
(1) the current agent training corpus is entangled with both formats following
and agent reasoning, which significantly shifts from the distribution of its
pre-training data; (2) LLMs exhibit different learning speeds on the
capabilities required by agent tasks; and (3) current approaches have
side-effects when improving agent abilities by introducing hallucinations.
Based on the above findings, we propose Agent-FLAN to effectively Fine-tune
LANguage models for Agents. Through careful decomposition and redesign of the
training corpus, Agent-FLAN enables Llama2-7B to outperform prior best works by
3.5\% across various agent evaluation datasets. With comprehensively
constructed negative samples, Agent-FLAN greatly alleviates the hallucination
issues based on our established evaluation benchmark. Besides, it consistently
improves the agent capability of LLMs when scaling model sizes while slightly
enhancing the general capability of LLMs. The code will be available at
https://github.com/InternLM/Agent-FLAN.
Agent-FLAN fine-tunes LLMs for agent tasks by reorganizing the training corpus, reducing hallucinations, and enhancing agent capability without degrading general performance.
Large Language Models LLMs agent training corpus agent reasoning agent tasks fine-tuning hallucinations Llama2-7B negative samples agent capability general capability