---
title: "HalluAudio — paper by Zhao et al. (2026) — summary and ideas"
description: "The authors build HalluAudio, a benchmark covering speech, environmental sound and music, with more than 5K QA pairs… 37 references."
authors: ["Feiyu Zhao", "Yiming Chen", "Wenhuan Lu", "Daipeng Zhang", "Xianghu Yue", "Jianguo Wei"]
year: 2026
venue: "Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)"
source: "https://aclanthology.org/2026.acl-long.1797/"
doi: "10.18653/v1/2026.acl-long.1797"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/zhao-2026-halluaudio-comprehensive-benchmark-hallucination-detection/"
---
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# HalluAudio: A Comprehensive Benchmark for Hallucination Detection in Large Audio-Language Models

**Authors:** Feiyu Zhao, [Yiming Chen](https://md.research.elamur.ai/free/en/authors/yiming-chen/), Wenhuan Lu, Daipeng Zhang, [Xianghu Yue](https://md.research.elamur.ai/free/en/authors/xianghu-yue/) and Jianguo Wei · **Year:** 2026 · **Venue:** [Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)](https://md.research.elamur.ai/free/en/venues/acl/) ([ACL 2026](https://md.research.elamur.ai/free/en/venues/acl/2026/)) · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/association-for-computational-linguistics/) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2026.acl-long.1797/) · [PDF](https://aclanthology.org/2026.acl-long.1797.pdf) · [DOI 10.18653/v1/2026.acl-long.1797](https://doi.org/10.18653/v1/2026.acl-long.1797) · [OpenAlex](https://openalex.org/W7166796202)

**Citations:** 0 citations (OpenAlex, 2026-10-14)

## Summary
Large Audio-Language Models (LALMs) often make claims that the input audio does not support. Hallucination research has mostly targeted text and images, and earlier audio efforts such as AHa-Bench are small and mostly binary. The authors build HalluAudio, a benchmark covering speech, environmental sound and music, with more than 5K QA pairs checked by humans. The items come from a five-stage pipeline: clip collection, task building, adversarial augmentation, automatic filtering and several rounds of manual review. Formats include yes/no questions, counting, multi-label classification, attribute checks, comparisons and open answers. Overlapped or mixed audio and deliberately invalid questions are used to provoke errors. Hallucination is defined as inventing events, contradicting clear acoustic structure, or giving unwarranted "yes" answers. Models are scored on accuracy, hallucination rate, Yes/No bias, error types and false refusals. Tests on open and commercial LALMs show that no model stays reliable across domains. High scores on ordinary audio benchmarks do not guarantee grounded answers, so reliability needs dedicated diagnostics.

## Key points
- Audio sources: Common Voice (speech), FSD50K (sound), and GTZAN plus Mridangam Strokes/Tonics (music). The data is English only.
- Tasks are grouped into three hallucination types: structural/temporal (Temporal Comparison), perceptual (Recognition) and semantic (Consistency). Evaluation is zero-shot, and outputs are normalized into Yes/No/Number/Refusal/Other.
- Qwen2.5-Omni, Qwen2-Audio and Kimi-Audio tend to over-answer "yes", especially on ordering and counting. This affirmative bias is strongest for environmental sound, where models must infer that an event is absent.
- Gemini-2.5-Flash and Qwen2-Audio refuse answerable questions most often. Phi-4-Multimodal, Kimi-Audio, MiMo-Audio and Step-Audio-2 rarely refuse in speech.
- Music produces the widest spread between models. GPT-4o-Audio and MiMo-Audio handle high-level semantic queries better, but most models struggle with musical counting and order.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

## Institutions

- [Tianjin University](https://md.research.elamur.ai/free/en/universities/tianjin-university/) — University, Tianjin, China · 52 works · 1,128 citations

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## Cite

Cite as: Feiyu Zhao, Yiming Chen, Wenhuan Lu, Daipeng Zhang, Xianghu Yue and Jianguo Wei. 2026. HalluAudio: A Comprehensive Benchmark for Hallucination Detection in Large Audio-Language Models. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers).

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{zhao2026halluaudio,
  title = {{HalluAudio: A Comprehensive Benchmark for Hallucination Detection in Large Audio-Language Models}},
  author = {Feiyu Zhao, Yiming Chen, Wenhuan Lu, Daipeng Zhang, Xianghu Yue and Jianguo Wei},
  booktitle = {Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  year = {2026},
  doi = {10.18653/v1/2026.acl-long.1797},
  url = {https://doi.org/10.18653/v1/2026.acl-long.1797}
}
```

- [← previous HalleluBERT: Let Every Token That Has Meaning Bear Its Weight](https://md.research.elamur.ai/free/en/papers/schmitt-2026-hallelubert-let-every-token-that/)
- [next → Hallucinations as Orthogonal Noise: Inference-Time Manifold Alignment via Dynamic Contextual Orthogonalization](https://md.research.elamur.ai/free/en/papers/zhao-2026-hallucinations-as-orthogonal-noise-inference/)
- [↑ back to papers of 2026](https://md.research.elamur.ai/free/en/papers/2026/page/35/)

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*Updated 2 November 2026*