Walking Through Uncertainty: An Empirical Study of Uncertainty Estimation for Audio-Aware Large Language Models
Abstract
Recent audio-aware large language models (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 following analysis is generated by AI and may contain errors. It is separate from the authors’ abstract and is not peer review.
Research summary
This paper makes a significant contribution by systematically evaluating uncertainty estimation methods in audio-aware large language models, revealing critical insights that could guide future research and applications in multimodal AI systems. The comprehensive benchmarking and analysis of methods provide a valuable foundation for improving the reliability of ALLMs in practical scenarios.
Methodology
The paper presents a systematic empirical study of uncertainty estimation methods tailored for audio-aware large language models (ALLMs). It benchmarks five distinct methods, including predictive entropy and semantic entropy, across various models and tasks, highlighting the unique challenges posed by audio inputs. The methodology is sound, employing a two-stage protocol for uncertainty estimation and a clear comparative analysis across multiple benchmarks. However, the reliance on existing methods from text-based LLMs without significant adaptation for audio-specific challenges could be seen as a limitation.
Results & evaluation
The experiments are comprehensive, covering a wide range of benchmarks that assess both general audio understanding and trustworthiness-oriented tasks. The results indicate that semantic-level and verification-based methods consistently outperform token-level baselines, providing valuable insights into the performance of uncertainty estimation in ALLMs. The evaluation metrics, including AUROC and AURAC, are appropriate for the tasks at hand.
Reproducibility
While the paper provides a detailed description of the experimental setup, including the models used and the evaluation protocols, it lacks specific implementation details or code availability, which could hinder reproducibility. The absence of a project URL further complicates this aspect.
Limitations
The study primarily focuses on constrained answer spaces, which may not generalize well to open-ended tasks. Additionally, the uncertainty estimation methods are largely inherited from text LLM literature, potentially limiting their effectiveness in capturing audio-specific uncertainties. The fixed threshold for adaptive inference may not be optimal across all scenarios, and the study does not explore more sophisticated routing strategies.
Broader impact
The findings have significant implications for the development of more reliable audio-language systems, particularly in applications requiring robust uncertainty estimation for decision-making. The work lays a foundation for future research in uncertainty-aware models, which could enhance the safety and reliability of AI systems in high-stakes environments. This paper makes a significant contribution by systematically evaluating uncertainty estimation methods in audio-aware large language models, revealing critical insights that could guide future research and applications in multimodal AI systems. The comprehensive benchmarking and analysis of methods provide a valuable foundation for improving the reliability of ALLMs in practical scenarios.
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