Confucius: Iterative Tool Learning from Introspection Feedback by Easy-to-Difficult Curriculum

• URL: http://arxiv.org/abs/2308.14034v2
• Date: Thu, 21 Dec 2023 07:30:31 GMT
• Title: Confucius: Iterative Tool Learning from Introspection Feedback by Easy-to-Difficult Curriculum
• Authors: Shen Gao, Zhengliang Shi, Minghang Zhu, Bowen Fang, Xin Xin, Pengjie Ren, Zhumin Chen, Jun Ma, Zhaochun Ren
• Abstract summary: We propose a novel tool learning framework to train large language models (LLMs) to use complicated tools in real-world scenarios. We first propose a multi-stage learning method to teach the LLM to use various tools from an easy-to-difficult curriculum. We then propose the Iterative Self-instruct from Introspective Feedback to dynamically construct the dataset to improve the ability to use the complicated tool.
• Score: 42.36892453363961
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Augmenting large language models (LLMs) with external tools has emerged as a promising approach to extending the capability of LLMs. Although some works employ open-source LLMs for the tool learning task, most of them are trained in a controlled environment in which LLMs only learn to execute the human-provided tools. However, selecting proper tools from the large toolset is also a crucial ability for the tool learning model to be applied in real-world applications. Existing methods usually directly employ self-instruction methods to train the model, which ignores differences in tool complexity. In this paper, we propose the Confucius, a novel tool learning framework to train LLM to use complicated tools in real-world scenarios, which contains two main phases: (1) We first propose a multi-stage learning method to teach the LLM to use various tools from an easy-to-difficult curriculum; (2) thenceforth, we propose the Iterative Self-instruct from Introspective Feedback (ISIF) to dynamically construct the dataset to improve the ability to use the complicated tool. Extensive experiments conducted on both controlled and real-world settings demonstrate the superiority of our tool learning framework in the real-world application scenarios compared to both tuning-free (e.g. ChatGPT, Claude) and tuning-based baselines (e.g. GPT4Tools).

Related papers

• StepTool: A Step-grained Reinforcement Learning Framework for Tool Learning in LLMs [44.906714156993694]
  We introduce StepTool, a novel step-grained reinforcement learning framework to improve tool learning in Large Language Models. StepTool significantly outperforms existing methods in multi-step, tool-based tasks.
  arXiv  Detail & Related papers  (2024-10-10T09:23:26Z)
• Chain of Tools: Large Language Model is an Automatic Multi-tool Learner [54.992464510992605]
  Automatic Tool Chain (ATC) is a framework that enables the large language models (LLMs) to act as a multi-tool user. To scale up the scope of the tools, we next propose a black-box probing method. For a comprehensive evaluation, we build a challenging benchmark named ToolFlow.
  arXiv  Detail & Related papers  (2024-05-26T11:40:58Z)
• Towards Completeness-Oriented Tool Retrieval for Large Language Models [60.733557487886635]
  Real-world systems often incorporate a wide array of tools, making it impractical to input all tools into Large Language Models. Existing tool retrieval methods primarily focus on semantic matching between user queries and tool descriptions. We propose a novel modelagnostic COllaborative Learning-based Tool Retrieval approach, COLT, which captures not only the semantic similarities between user queries and tool descriptions but also takes into account the collaborative information of tools.
  arXiv  Detail & Related papers  (2024-05-25T06:41:23Z)
• LLMs in the Imaginarium: Tool Learning through Simulated Trial and Error [54.954211216847135]
  Existing large language models (LLMs) only reach a correctness rate in the range of 30% to 60%. We propose a biologically inspired method for tool-augmented LLMs, simulated trial and error (STE) STE orchestrates three key mechanisms for successful tool use behaviors in the biological system: trial and error, imagination, and memory.
  arXiv  Detail & Related papers  (2024-03-07T18:50:51Z)
• Look Before You Leap: Towards Decision-Aware and Generalizable Tool-Usage for Large Language Models [26.28459880766842]
  We propose a decision-aware and generalizable tool-usage framework (DEER) Specifically, we first construct the tool-usage samples with multiple decision branches via an automatic generation pipeline. Our proposed DEER is effective and significantly outperforms baselines across various datasets.
  arXiv  Detail & Related papers  (2024-02-26T16:11:03Z)
• Small LLMs Are Weak Tool Learners: A Multi-LLM Agent [73.54562551341454]
  Large Language Model (LLM) agents significantly extend the capabilities of standalone LLMs. We propose a novel approach that decomposes the aforementioned capabilities into a planner, caller, and summarizer. This modular framework facilitates individual updates and the potential use of smaller LLMs for building each capability.
  arXiv  Detail & Related papers  (2024-01-14T16:17:07Z)
• EASYTOOL: Enhancing LLM-based Agents with Concise Tool Instruction [56.02100384015907]
  EasyTool is a framework transforming diverse and lengthy tool documentation into a unified and concise tool instruction. It can significantly reduce token consumption and improve the performance of tool utilization in real-world scenarios.
  arXiv  Detail & Related papers  (2024-01-11T15:45:11Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.