HalluAudio: A Comprehensive Benchmark for Hallucination Detection in Large Audio-Language Models
Abstract
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.
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
The paper presents HalluAudio, a comprehensive benchmark for evaluating hallucination detection in Large Audio-Language Models, addressing a critical gap in the audio domain. The innovative methodology and thorough experimental evaluation contribute significantly to the understanding of model behavior, making it a valuable resource for future research in audio processing and machine learning.
Methodology
The methodology is robust, featuring a systematic approach to constructing the HalluAudio benchmark, which includes a five-step pipeline for data collection and validation. The use of adversarial prompts and mixed-audio conditions to induce hallucinations is particularly innovative, allowing for a nuanced exploration of model behavior across various audio tasks. The incorporation of multiple task types and the detailed analysis of hallucination behaviors add depth to the evaluation process.
Results & evaluation
The experimental evaluation is comprehensive, benchmarking a wide range of LALMs across three audio domains with over 5,000 human-verified QA pairs. The results reveal significant deficiencies in current models, highlighting specific failure modes such as acoustic grounding and temporal reasoning. The analysis of Yes/No bias and false refusal rates provides valuable insights into model behavior beyond mere accuracy, making the findings relevant and actionable for future research.
Reproducibility
The paper outlines a clear methodology for dataset construction and evaluation, which enhances reproducibility. The use of a human-in-the-loop approach for validation ensures high-quality data, and the detailed description of the evaluation metrics allows for replication of the experiments. However, the actual implementation details of the models evaluated are not provided, which could hinder complete reproducibility.
Limitations
One limitation is the potential bias introduced by the specific audio clips selected for the benchmark, which may not represent the full diversity of audio scenarios encountered in real-world applications. Additionally, while the benchmark is comprehensive, it may not cover all possible hallucination scenarios, leaving some gaps in the evaluation of LALMs. The reliance on human verification, while ensuring quality, may also introduce subjectivity.
Broader impact
The introduction of HalluAudio has the potential to significantly impact the development of more reliable LALMs by providing a standardized framework for evaluating hallucination behaviors. This benchmark could guide researchers in identifying and addressing the limitations of current models, ultimately leading to improvements in audio understanding and reasoning capabilities in practical applications. The paper presents HalluAudio, a comprehensive benchmark for evaluating hallucination detection in Large Audio-Language Models, addressing a critical gap in the audio domain. The innovative methodology and thorough experimental evaluation contribute significantly to the understanding of model behavior, making it a valuable resource for future research in audio processing and machine learning.
Author of this paper? Send a correction.