LLMs often misalign their self-perceived need for tools with true need and utility, but lightweight estimators trained on hidden states can improve tool-calling decisions and task performance across multiple models and tasks.
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2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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2026 2verdicts
UNVERDICTED 2representative citing papers
The paper defines algorithmic contestability as identifying evidence to overturn potentially incorrect decisions and identifies three types of such evidence that make decisions normatively indefensible under the decision maker's standards.
citing papers explorer
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To Call or Not to Call: A Framework to Assess and Optimize LLM Tool Calling
LLMs often misalign their self-perceived need for tools with true need and utility, but lightweight estimators trained on hidden states can improve tool-calling decisions and task performance across multiple models and tasks.
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Explainable AI Isn't Enough! Rethinking Algorithmic Contestability
The paper defines algorithmic contestability as identifying evidence to overturn potentially incorrect decisions and identifies three types of such evidence that make decisions normatively indefensible under the decision maker's standards.