Walking Through Uncertainty: An Empirical Study of Uncertainty Estimation for Audio-Aware Large Language Models

Abstract: Recent audio-aware LLMs (ALLMs) have demonstrated strong capabilities across diverse audio understanding and reasoning tasks, but they still frequently produce hallucinated or overly confident outputs. While uncertainty estimation has been extensively studied in text-only LLMs, it remains largely unexplored for ALLMs, where audio-conditioned generation introduces additional challenges such as perceptual ambiguity and cross-modal grounding. In this work, we present the first systematic empirical study of uncertainty estimation in ALLMs. We benchmark five representative methods, including predictive entropy, length-normalized entropy, semantic entropy, discrete semantic entropy, and P(True), across multiple models and diverse evaluation settings spanning general audio understanding, reasoning, hallucination detection, and unanswerable question answering. Our results reveal two key findings. First, semantic-level and verification-based methods consistently outperform token-level baselines on general audio reasoning benchmarks. Second, on trustworthiness-oriented benchmarks, the relative effectiveness of uncertainty methods becomes notably more model- and benchmark-dependent, indicating that conclusions drawn from general reasoning settings do not straightforwardly transfer to hallucination and unanswerable-question scenarios. We further explore uncertainty-based adaptive inference as a potential downstream application. We hope this study provides a foundation for future research on reliable, uncertainty-aware audio-language systems.

• The paper demonstrates that semantic-based and self-verification methods outperform token-likelihood approaches in uncertainty estimation for audio-aware tasks.
• It employs a two-stage decoding strategy to benchmark five uncertainty techniques across audio reasoning and trustworthiness-oriented benchmarks.
• Adaptive inference driven by uncertainty metrics achieves comparable accuracy with 24–64% token cost, underscoring the potential for cost-efficient deployment.

Uncertainty Estimation in Audio-Aware LLMs: Empirical Analysis and Implications

Introduction and Context

Audio-aware LLMs (ALLMs) have achieved notable advancements in multimodal reasoning by conditioning generation on both text and audio signals, thereby extending traditional LLMs to complex, perceptually grounded tasks. However, the growing deployment of ALLMs in practical settings underscores challenges in reliability: models often produce hallucinated or unsupported outputs and are prone to overly confident reasoning even when the underlying audio evidence is ambiguous or insufficient. While text-only LLMs have been systematically studied with respect to uncertainty estimation, the unique setting of ALLMs—where predictions must be grounded in both linguistic and auditory modalities—presents additional complexity. The paper "Walking Through Uncertainty: An Empirical Study of Uncertainty Estimation for Audio-Aware LLMs" (2604.25591) addresses this critical gap by conducting the first systematic empirical analysis of uncertainty quantification techniques in ALLMs, contrasting general reasoning with trustworthiness-oriented settings.

Methodological Approach

The authors benchmark five representative uncertainty estimation approaches:

• Predictive Entropy: Assesses uncertainty via the sequence-level likelihood of generated responses.
• Length-normalized Predictive Entropy: Mitigates length bias by normalizing entropy over output tokens.
• Semantic Entropy: Employs bidirectional entailment clustering to group semantically equivalent answers, measuring distributional dispersion over meaning clusters.
• Discrete Semantic Entropy: Specialized for settings with a closed answer set, estimating entropy over the empirical answer distribution.
• P(True): Prompts the model for self-verification, producing a binary confidence estimate of correctness.

Evaluations utilize a two-stage protocol: first, low-temperature decoding is employed to generate the model’s final answer; then uncertainty is estimated by sampling multiple responses (high-temperature decoding) for scoring. Experiments proceed across a suite of audio understanding and reasoning benchmarks (e.g., MMAU, MMAR, MMSU, SAKURA) and trustworthiness-oriented tasks focused on hallucination and unanswerable question identification (e.g., Audio-Hallucination, AQUA-Bench). State-of-the-art ALLMs evaluated include Qwen2.5-Omni-3B/7B and Audio Flamingo 3.

Empirical Findings: Reasoning and Trustworthiness Contexts

General Audio Reasoning and Understanding

For standard audio-language reasoning benchmarks, the empirical results consistently demonstrate that semantic-based and verification-based methods (both semantic entropy variants and P(True)) outperform token-likelihood-based approaches. Notably, AUROC and AURAC scores substantiate the superior discriminative ability of these methods in distinguishing correct from incorrect predictions and in supporting selective prediction.

The competitive performance of semantic entropy and P(True) is attributable to their capacity for evaluating answer uncertainty at the level of semantic content or explicit model self-assessment, both of which are crucial when correctness is linked to grounded perceptual evidence. In contrast, token-level entropy—dominated by surface-form likelihoods—fails to reliably detect failures stemming from hallucination or weak audio grounding.

Trustworthiness-Oriented Settings

When the evaluation shifts to trustworthiness-centric tasks (hallucination detection, unanswerable questions), the authors report notably increased benchmark- and model-dependence in uncertainty method efficacy. Here, no single uncertainty estimate is universally optimal: in some model-benchmark combinations, semantic entropy remains competitive, while in others, normalized entropy or self-verification-based P(True) is dominant.

This divergence highlights a contradiction with standard reasoning tasks, where semantic-based methods are universally advantageous. It reveals the non-triviality of transferring uncertainty estimation insights from conventional to reliability-centric benchmarks—likely due to nuanced differences in how models represent and express uncertainty in the absence of unambiguous audio support.

Adaptive Inference: Cost-Efficiency Trade-offs

The study introduces uncertainty-driven adaptive inference, where uncertainty estimates route inputs to either a direct or a more compute-intensive reasoning mode according to a predetermined threshold. This strategy can achieve a more favorable cost-accuracy Pareto frontier, as demonstrated in cost-accuracy analyses:

Figure 1: Cost–accuracy trade-off for Reasoning (hollow squares) vs. Adaptive inference (filled circles) across benchmarks, with Adaptive inference shifting models toward lower cost at comparable or improved performance.

When the fallback reasoning mode is genuinely beneficial, adaptive inference yields improved or matched top-1 accuracy with 24–64% of the token cost required for reasoning over all examples. However, if the reasoning strategy is not itself strictly superior to direct inference (e.g., on MMAR), this approach does not improve performance, highlighting the conditional utility of uncertainty-based routing.

Reliability Calibration

The authors offer extended analysis of capability calibration, quantifying whether ALLMs' self-assigned confidence aligns with expected correctness prior to answering. Calibration is benchmark- and domain-dependent; for instance, perceptual subtasks yield systematically higher calibration errors versus reasoning subtasks. This asymmetry suggests that models overestimate perceptual competence relative to reasoning, an effect observable in reliability diagrams (see supplementary reliability plots referenced in the text). These findings underscore structural limitations in current ALLM architectures with respect to modality-specific uncertainty.

Theoretical and Practical Implications

• Superiority of Semantic-based Uncertainty: The empirical dominance of semantic entropy and verification signals over token-level methods echoes similar findings in text-only LLMs [kuhnsemantic, kadavath2022language], but is further magnified in multimodal settings where lexical variations are less meaningful than semantic fidelity to perceptual input.
• Task and Model Sensitivity: Unlike general reasoning, reliability-centric scenarios (hallucination, abstention) expose sharp model-dependent variation, implying that universal uncertainty heuristics are insufficient and that tailored evaluation and calibration protocols are needed.
• Cost-Efficiency via Adaptivity: Uncertainty-based adaptive inference directly addresses the high compute cost of exhaustive reasoning, but only when the alternative reasoning path confers genuine value. This warns against default application of "deeper thinking" pipelines in multimodal LLM deployments [ghosal2025does].
• Training and Evaluation Directions: There is scope for developing audio-modality-aware uncertainty estimation methods, integrating uncertainty as a learning signal to prioritize challenging cases, and refining calibration via representation-level (not just output-level) signals [kostumov2024uncertainty].

Conclusion

The paper systematically characterizes uncertainty estimation in ALLMs, showing semantic clustering and self-verification as robust error signals for general reasoning, while revealing the model- and benchmark-sensitivity of uncertainty for trustworthiness evaluation. Adaptive inference, routed by uncertainty, delivers more computation-efficient deployment when the reasoning mode is reliable. These results call for future work in audio-modality-aware uncertainty modeling, cross-benchmark calibration, and principled integration of uncertainty signals in training and inference pipelines to enable reliable, cost-effective multimodal AI systems.