Can Tool-augmented Large Language Models be Aware of Incomplete Conditions?

• URL: http://arxiv.org/abs/2406.12307v2
• Date: Sun, 29 Sep 2024 05:11:45 GMT
• Title: Can Tool-augmented Large Language Models be Aware of Incomplete Conditions?
• Authors: Seungbin Yang, ChaeHun Park, Taehee Kim, Jaegul Choo,
• Abstract summary: This study examines whether large language models can identify incomplete conditions and appropriately determine when to refrain from using tools. We confirm that most LLMs are challenged to identify the additional information required to utilize specific tools and the absence of appropriate tools.
• Score: 33.74511128798095
• License:
• Abstract: Recent advancements in integrating large language models (LLMs) with tools have allowed the models to interact with real-world environments. However, these \textit{tool-augmented LLMs} often encounter incomplete scenarios when users provide partial information or the necessary tools are unavailable. Recognizing and managing such scenarios is crucial for LLMs to ensure their reliability, but this exploration remains understudied. This study examines whether LLMs can identify incomplete conditions and appropriately determine when to refrain from using tools. To this end, we address a dataset by manipulating instances from two datasets by removing necessary tools or essential information for tool invocation. We confirm that most LLMs are challenged to identify the additional information required to utilize specific tools and the absence of appropriate tools. We further analyze model behaviors in different environments and compare their performance against humans. Our research can contribute to advancing reliable LLMs by addressing scenarios that commonly arise during interactions between humans and LLMs.

Related papers

• Learning to Ask: When LLMs Meet Unclear Instruction [49.256630152684764]
  Large language models (LLMs) can leverage external tools for addressing a range of tasks unattainable through language skills alone. We evaluate the performance of LLMs tool-use under imperfect instructions, analyze the error patterns, and build a challenging tool-use benchmark called Noisy ToolBench. We propose a novel framework, Ask-when-Needed (AwN), which prompts LLMs to ask questions to users whenever they encounter obstacles due to unclear instructions.
  arXiv  Detail & Related papers  (2024-08-31T23:06:12Z)
• WTU-EVAL: A Whether-or-Not Tool Usage Evaluation Benchmark for Large Language Models [31.742620965039517]
  Large Language Models (LLMs) excel in NLP tasks, but still need external tools to extend their ability. We introduce the Whether-or-not tool usage Evaluation benchmark (WTU-Eval) to assess LLMs with eleven datasets. The results of eight LLMs on WTU-Eval reveal that LLMs frequently struggle to determine tool use in general datasets. Fine-tuning Llama2-7B results in a 14% average performance improvement and a 16.8% decrease in incorrect tool usage.
  arXiv  Detail & Related papers  (2024-07-02T12:07:38Z)
• 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)
• Towards Practical Tool Usage for Continually Learning LLMs [28.62382804829694]
  Large language models show an innate skill for solving language based tasks. But their knowledge, stored directly within their parameters, remains static in time. Tool use helps by offloading work to systems that the LLM can access through an interface. But LLMs that use them still must adapt to nonstationary environments for prolonged use.
  arXiv  Detail & Related papers  (2024-04-14T19:45:47Z)
• 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)
• Planning, Creation, Usage: Benchmarking LLMs for Comprehensive Tool Utilization in Real-World Complex Scenarios [93.68764280953624]
  UltraTool is a novel benchmark designed to improve and evaluate Large Language Models' ability in tool utilization. It emphasizes real-world complexities, demanding accurate, multi-step planning for effective problem-solving. A key feature of UltraTool is its independent evaluation of planning with natural language, which happens before tool usage.
  arXiv  Detail & Related papers  (2024-01-30T16:52:56Z)
• 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)
• ToolEyes: Fine-Grained Evaluation for Tool Learning Capabilities of Large Language Models in Real-world Scenarios [48.38419686697733]
  We propose ToolEyes, a fine-grained system tailored for the evaluation of large language models' tool learning capabilities in authentic scenarios. The system meticulously examines seven real-world scenarios, analyzing five dimensions crucial to LLMs in tool learning. ToolEyes incorporates a tool library boasting approximately 600 tools, serving as an intermediary between LLMs and the physical world.
  arXiv  Detail & Related papers  (2024-01-01T12:49:36Z)
• MetaTool Benchmark for Large Language Models: Deciding Whether to Use Tools and Which to Use [82.24774504584066]
  Large language models (LLMs) have garnered significant attention due to their impressive natural language processing (NLP) capabilities. We introduce MetaTool, a benchmark designed to evaluate whether LLMs have tool usage awareness and can correctly choose tools. We conduct experiments involving eight popular LLMs and find that the majority of them still struggle to effectively select tools.
  arXiv  Detail & Related papers  (2023-10-04T19:39:26Z)

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.