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

Researchers identify structural alignment bias, a mechanistic flaw where large language models invoke tools even when irrelevant to user queries, simply because query attributes match tool parameters. The study introduces SABEval dataset and a rebalancing strategy that effectively mitigates this bias without degrading general tool-use capabilities.

Large language models have become increasingly sophisticated at utilizing external tools, yet this capability masks a critical vulnerability in their decision-making processes. The research reveals that LLMs suffer from structural alignment bias—a tendency to invoke tools based on parameter matching rather than semantic relevance. This flaw emerges even when tools demonstrably fail to serve user objectives, suggesting LLMs conflate syntactic compatibility with functional appropriateness.

The broader context stems from the rapid integration of tool-calling capabilities into LLM architectures. As systems like GPT-4, Claude, and others expanded their ability to access external APIs and functions, the assumption largely held that these models would intelligently discern when tools are necessary. This research challenges that assumption, demonstrating that existing evaluation frameworks systematically overlook this bias, creating a gap between perceived and actual performance.

For developers building LLM-powered applications, this finding carries immediate implications. Production systems relying on tool invocation for agentic workflows may exhibit unexpected behavior—invoking irrelevant APIs, executing unnecessary function calls, or wasting computational resources. The introduction of Contrastive Attention Attribution provides a window into the competing neural pathways driving invocation decisions, revealing that semantic checking and structural matching operate in tension rather than harmony.

The proposed rebalancing strategy addresses this vulnerability without sacrificing general tool-use capabilities, suggesting that developers can implement mitigations without sacrificing overall system performance. Going forward, evaluating LLM tool use requires more sophisticated benchmarks that explicitly test semantic relevance alongside structural compatibility, reshaping how researchers assess and deploy these systems in production environments.

• →LLMs invoke tools based on structural parameter alignment even when semantically irrelevant to user queries, creating a widespread mechanistic flaw.
• →Existing evaluation frameworks fail to account for structural alignment bias, masking performance gaps in production LLM applications.
• →Contrastive Attention Attribution reveals two competing neural pathways—semantic checking and structural matching—that determine tool invocation decisions.
• →A proposed rebalancing strategy effectively mitigates structural alignment bias without degrading general tool-use capabilities.
• →This research suggests LLM tool-calling evaluations require more sophisticated benchmarks testing semantic relevance alongside structural compatibility.