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Reducing Tool Hallucination via Reliability Alignment
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Large Language Models (LLMs) have expanded their capabilities beyond language generation to interact with external tools, enabling automation and real-world applications. However, tool hallucinations, where models either select inappropriate tools or misuse them, pose significant challenges, leading to erroneous task execution, increased computational costs, and reduced system reliability. To systematically address this issue, we define and categorize tool hallucinations into two main types, tool selection hallucination and tool usage hallucination. To evaluate and mitigate these issues, we introduce RelyToolBench, which integrates specialized test cases and novel metrics to assess hallucination-aware task success and efficiency. Finally, we propose Relign, a reliability alignment framework that expands the tool-use action space to include indecisive actions, allowing LLMs to defer tool use, seek clarification, or adjust tool selection dynamically. Through extensive experiments, we demonstrate that Relign significantly reduces tool hallucinations, improves task reliability, and enhances the efficiency of LLM tool interactions.
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Cited by 4 Pith papers
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PredAct-Bench: Benchmarking Tool-Augmented Dialogue under Controlled Tool Noise
When grade-prediction tools are noisy, most LLM instructors over-rely on them in multi-turn dialogue and their decisions degrade, whereas human instructors stay better calibrated.
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Multi-Head Latent Control: A Unified Interface for LLM Agent Decision Making
Hidden-state traces of frozen LLMs/VLMs can be read by lightweight trained heads to predict when to defer, clarify, call tools, or abstain, cutting routed inference cost 27–90%.
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The Reasoning Trap: How Enhancing LLM Reasoning Amplifies Tool Hallucination
Strengthening LLM reasoning through RL, SFT, or chain-of-thought prompting increases tool hallucination rates on SimpleToolHalluBench, with a reliability-capability trade-off observed across mitigation attempts.
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Learning to Act under Noise: Enhancing Agent Robustness via Noisy Environments
NoisyAgent trains LLM agents with controlled user and tool noise to improve robustness in stochastic environments while also boosting clean-benchmark performance.
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