When Tools Get in the Way: The Effect of Unnecessary Tool Availability on LLM Answering

2026-09-12

AI Summary

研究探讨了不必要的工具可用性对大型语言模型从自身知识回答问题能力的影响，通过构建500组查询对比实验来评估六种模型的表现，并提出一种系统指令以恢复受影响的回答率。

Abstract

Large language models (LLMs) are increasingly deployed with external tools that extend what they can do beyond their own knowledge. Tools help on tasks that need external information, but their availability may also change how a model handles questions that do not need them. Prior work has mostly asked whether models select and use tools appropriately; whether an unnecessary tool changes the correctness of answers has received less attention. We ask whether making a related but unnecessary tool available affects a model's ability to answer from its own knowledge, and whether a preceding tool interaction changes this behaviour. We construct 500 query pairs across 10 knowledge domains. Each pair consists of a tool query, which needs the domain's tool, and a closed-domain query, which does not. Six LLMs are evaluated with the tool unavailable, available, and available after a prior tool call. Across 3,000 baseline trials the pooled answer rate is 98.2%. When an unnecessary tool is available it falls to 63.5%, with large differences between models. The decrease occurs even when the tool is rarely called, so it cannot be explained by unnecessary tool invocation alone. A one-sentence scope-aware system instruction recovers most of the lost answers.

Problem

• large language models
• external tools
• unnecessary tool availability
• answer correctness
• tool interaction

Innovation

• Large Language Models
• Tool Availability
• Answer Accuracy
• Scope-aware Instruction