# HalluAudio: A Comprehensive Benchmark for Hallucination Detection in Large Audio-Language Models

arXiv: https://arxiv.org/abs/2604.19300 · HTML: https://mlsanity.com/paper/2604.19300/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 26 references that are in the map.
- First posted on arXiv 21 Apr 2026.

## Builds on (most cited first, 15 of 15)

- [TruthfulQA: Measuring How Models Mimic Human Falsehoods](https://mlsanity.com/paper/2109.07958/) (2021)
- [Common Voice: A Massively-Multilingual Speech Corpus](https://mlsanity.com/paper/1912.06670/) (2019)
- [AudioLM: a Language Modeling Approach to Audio Generation](https://mlsanity.com/paper/2209.03143/) (2022)
- [FSD50K: An Open Dataset of Human-Labeled Sound Events](https://mlsanity.com/paper/2010.00475/) (2020)
- [Pengi: An Audio Language Model for Audio Tasks](https://mlsanity.com/paper/2305.11834/) (2023)
- [AIR-Bench: Benchmarking Large Audio-Language Models via Generative Comprehension](https://mlsanity.com/paper/2402.07729/) (2024)
- [VoiceBench: Benchmarking LLM-Based Voice Assistants](https://mlsanity.com/paper/2410.17196/) (2024)
- [Audio Flamingo: A Novel Audio Language Model with Few-Shot Learning and Dialogue Abilities](https://mlsanity.com/paper/2402.01831/) (2024)
- [HEAR: Holistic Evaluation of Audio Representations](https://mlsanity.com/paper/2203.03022/) (2022)
- [LLaMA-Omni2: LLM-based Real-time Spoken Chatbot with Autoregressive Streaming Speech Synthesis](https://mlsanity.com/paper/2505.02625/) (2025)
- [MiMo-Audio: Audio Language Models are Few-Shot Learners](https://mlsanity.com/paper/2512.23808/) (2025)
- [AutoHallusion: Automatic Generation of Hallucination Benchmarks for Vision-Language Models](https://mlsanity.com/paper/2406.10900/) (2024)
- [Can Large Audio-Language Models Truly Hear? Tackling Hallucinations with Multi-Task Assessment and Stepwise Audio Reasoning](https://mlsanity.com/paper/2410.16130/) (2024)
- [Hallucinations in Neural Automatic Speech Recognition: Identifying Errors and Hallucinatory Models](https://mlsanity.com/paper/2401.01572/) (2024)
- [LoASR-Bench: Evaluating Large Speech Language Models on Low-Resource Automatic Speech Recognition Across Language Families](https://mlsanity.com/paper/2603.20042/) (2026)

## Built on by (most cited first, 8 of 8)

- [Walking Through Uncertainty: An Empirical Study of Uncertainty Estimation for Audio-Aware Large Language Models](https://mlsanity.com/paper/2604.25591/) (2026)
- [Adaptive Perturbation Selection for Contrastive Audio Decoding](https://mlsanity.com/paper/2607.00247/) (2026)
- [SURE-Voice: A Front-End Baseline for Speech-Evidence Filtering in Speech LLMs](https://mlsanity.com/paper/2608.27783/) (2026)
- [Auditing generative audio calls for known-task audio-llm evaluation](https://mlsanity.com/paper/2608.27817/) (2026)
- [AURA: Uncertainty-Routed Activation Editing for Acoustic Grounding in Speech Foundation Models](https://mlsanity.com/paper/2609.23979/) (2026)
- [Broadening Uncertainty Estimation for Audio Question Answering Across Methods, Formats, and Inputs](https://mlsanity.com/paper/2609.28879/) (2026)
- [MISHAP-Bench: A Hallucination Benchmark for Large Audio-Language Models](https://mlsanity.com/paper/2609.33893/) (2026)
- [Does Location Matter? A Black-Box Causal Audit Reveals Model-Dependent Audio Grounding in LALM Hallucination](https://mlsanity.com/paper/2610.13112/) (2026)

## Abstract (arXiv)

Large Audio-Language Models (LALMs) have recently achieved strong performance across various audio-centric tasks. However, hallucination, where models generate responses that are semantically incorrect or acoustically unsupported, remains largely underexplored in the audio domain. Existing hallucination benchmarks mainly focus on text or vision, while the few audio-oriented studies are limited in scale, modality coverage, and diagnostic depth. We therefore introduce HalluAudio, the first large-scale benchmark for evaluating hallucinations across speech, environmental sound, and music. HalluAudio comprises over 5K human-verified QA pairs and spans diverse task types, including binary judgments, multi-choice reasoning, attribute verification, and open-ended QA. To systematically induce hallucinations, we design adversarial prompts and mixed-audio conditions. Beyond accuracy, our evaluation protocol measures hallucination rate, yes/no bias, error-type analysis, and refusal rate, enabling a fine-grained analysis of LALM failure modes. We benchmark a broad range of open-source and proprietary models, providing the first large-scale comparison across speech, sound, and music. Our results reveal significant deficiencies in acoustic grounding, temporal reasoning, and music attribute understanding, underscoring the need for reliable and robust LALMs.