title: Agent-FLAN: Designing Data and Methods of Effective Agent Tuning for
    Large Language Models
summary: 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.
paper_authors._id: 65fa6243180d06d37c8528cf
paper_authors.name: Zehui Chen
paper_authors.status: admin_assigned
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paper_authors.fullname: Zehui Chen
paper_authors.type: user
paper_authors.user: lovesnowbest
paper_authors._id: 65fa6243180d06d37c8528d0
paper_authors.name: Kuikun Liu
paper_authors.status: claimed_verified
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paper_authors.fullname: liu
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paper_authors.user: Harold-lkk
paper_authors._id: 65fa6243180d06d37c8528d1
paper_authors.name: Qiuchen Wang
paper_authors.status: claimed_verified
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paper_authors.fullname: QiuchenWang
paper_authors.type: user
paper_authors.user: autumncc
paper_authors._id: 65fa6243180d06d37c8528d2
paper_authors.name: Wenwei Zhang
paper_authors.status: admin_assigned
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paper_authors.avatarUrl: /avatars/18958b8406d1ce492b54c1c839f18c54.svg
paper_authors.fullname: Wenwei Zhang
paper_authors.type: user
paper_authors.user: ZwwWayne
paper_authors._id: 65fa6243180d06d37c8528d3
paper_authors.name: Jiangning Liu
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-03-21T13:00:26.955Z
paper_authors._id: 65a7646581cc3017643a217d
paper_authors.avatarUrl: /avatars/ffc62e6f774e7d76b6a38aff432e01e4.svg
paper_authors.fullname: Jiangning Liu
paper_authors.type: user
paper_authors.user: liujiangning
paper_authors._id: 65fa6243180d06d37c8528d4
paper_authors.name: Dahua Lin
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-03-21T13:00:33.766Z
paper_authors._id: 636317ed80c1a705a6eff396
paper_authors.avatarUrl: /avatars/3db090e101b916d9256d0d3e043db71d.svg
paper_authors.fullname: Dahua Lin
paper_authors.type: user
paper_authors.user: lindahua
paper_authors._id: 65fa6243180d06d37c8528d5
paper_authors.name: Kai Chen
paper_authors._id: 65fa6243180d06d37c8528d6
paper_authors.name: Feng Zhao
paper_title: Agent-FLAN: Designing Data and Methods of Effective Agent Tuning for
    Large Language Models
paper_summary: 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.
paper_ai_summary: Agent-FLAN fine-tunes LLMs for agent tasks by reorganizing the training corpus, reducing hallucinations, and enhancing agent capability without degrading general performance.
paper_ai_keywords: Large Language Models LLMs agent training corpus agent reasoning agent tasks fine-tuning hallucinations Llama2-7B negative samples agent capability general capability