Uncertainty Estimation in Audio-Aware LLMs: First Systematic Study
An empirical investigation recently published on arXiv (2604.25591) presents the inaugural comprehensive assessment of uncertainty estimation in audio-aware large language models (ALLMs). This study evaluates five techniques—predictive entropy, length-normalized entropy, semantic entropy, discrete semantic entropy, and P(True)—across various models and tasks, including general audio comprehension, reasoning, hallucination detection, and answering unanswerable questions. Notable results reveal that approaches based on semantics and verification surpass simpler entropy methods in identifying hallucinations and uncertain results. The research also underscores specific challenges faced by ALLMs, such as perceptual ambiguity and cross-modal grounding, which make uncertainty measurement more complex than in text-only LLMs. This study fills a significant gap in the reliability of multimodal AI systems.
Key facts
- First systematic empirical study of uncertainty estimation in ALLMs
- Benchmarks five methods: predictive entropy, length-normalized entropy, semantic entropy, discrete semantic entropy, P(True)
- Evaluated across general audio understanding, reasoning, hallucination detection, and unanswerable QA
- Semantic-level and verification methods outperform entropy-based approaches
- ALLMs face additional challenges: perceptual ambiguity and cross-modal grounding
- Study published on arXiv with ID 2604.25591
- Addresses hallucination and overconfidence in audio-conditioned generation
- Cross-modal grounding is a key difficulty for uncertainty estimation
Entities
Institutions
- arXiv