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
3 Pith papers cite this work, alongside 11 external citations. Polarity classification is still indexing.
3
Pith papers citing it
11
external citations · external index
citation-role summary
background 1
citation-polarity summary
years
2026 3verdicts
UNVERDICTED 3roles
background 1polarities
background 1representative citing papers
Global calibration metrics like ECE are confounded by accuracy; the proposed ACE framework with three accuracy-controlled views shows many prior calibration advantages weaken or reverse.
Self-verification acts as a conditional confidence signal for language models rather than a reliable general-purpose uncertainty estimator.
citing papers explorer
-
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.
-
When Calibration Rankings Reverse: Accuracy-Controlled Evaluation for Fair Comparison of LLMs
Global calibration metrics like ECE are confounded by accuracy; the proposed ACE framework with three accuracy-controlled views shows many prior calibration advantages weaken or reverse.
-
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.