Research Paper#Audio-Language Models, Hallucination Reduction, Counterfactual Learning🔬 ResearchAnalyzed: Jan 3, 2026 16:51
AHA: Reducing Audio Hallucinations in Large Audio-Language Models
Published:Dec 30, 2025 07:52
•1 min read
•ArXiv
Analysis
This paper addresses the critical problem of hallucinations in Large Audio-Language Models (LALMs). It identifies specific types of grounding failures and proposes a novel framework, AHA, to mitigate them. The use of counterfactual hard negative mining and a dedicated evaluation benchmark (AHA-Eval) are key contributions. The demonstrated performance improvements on both the AHA-Eval and public benchmarks highlight the practical significance of this work.
Key Takeaways
- •Identifies and categorizes grounding failures (hallucinations) in LALMs.
- •Introduces the AHA framework to address these failures using counterfactual hard negative mining.
- •Develops AHA-Eval, a diagnostic benchmark for evaluating temporal reasoning.
- •Achieves significant performance improvements on both AHA-Eval and public benchmarks.
- •Demonstrates generalization beyond the diagnostic set.
Reference
“The AHA framework, leveraging counterfactual hard negative mining, constructs a high-quality preference dataset that forces models to distinguish strict acoustic evidence from linguistically plausible fabrications.”