Semantic-level and verification-based uncertainty methods outperform token-level baselines for audio reasoning in ALLMs, but their relative performance on hallucination and unanswerable-question benchmarks is model- and task-dependent.
Uncertainty in natural language processing: Sources, quantification, and applications
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Self-verification acts as a conditional confidence signal for language models rather than a reliable general-purpose uncertainty estimator.
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Walking Through Uncertainty: An Empirical Study of Uncertainty Estimation for Audio-Aware Large Language Models
Semantic-level and verification-based uncertainty methods outperform token-level baselines for audio reasoning in ALLMs, but their relative performance on hallucination and unanswerable-question benchmarks is model- and task-dependent.
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When Should a Language Model Trust Itself? Same-Model Self-Verification as a Conditional Confidence Signal
Self-verification acts as a conditional confidence signal for language models rather than a reliable general-purpose uncertainty estimator.