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Evidence record 5947 · automatically gathered

Do LLMs Know Tool Irrelevance? Demystifying Structural Alignment Bias in Tool Invocations

Large language models (LLMs) have demonstrated impressive capabilities in utilizing external tools. In practice, however, LLMs are often exposed to tools that are irrelevant to the user's query, in which case the desired behavior is to refrain from invocations. In this work, we identify a widespread yet overlooked mechanistic flaw in tool refusal, which we term structural alignment bias: Even when a tool fails to serve the user's goal, LLMs still tend to invoke it whenever query attributes can b

Record details

Published: 13 April 2026
Source: arXiv
Category: Research
Topics: Bias & fairness · Safety & alignment
Retrieved: 14 July 2026

source-onlyevidence status

These records share source-supplied organisations, an exact publisher byline, automatic topics or regions. The reason is shown on every link; related does not mean supporting, agreeing with or verifying this record.

Shared topic: Bias & fairness · Shared topic: Safety & alignment

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Shared topic: Bias & fairness · Shared topic: Safety & alignment

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How to cite this record

ethics.ai (13 April 2026), “Do LLMs Know Tool Irrelevance? Demystifying Structural Alignment Bias in Tool Invocations,” evidence record 5947, https://ethics.ai/record/5947 (originally published by arXiv).

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