All That Glitters Is Not Audio: Rethinking Text Priors and Audio Reliance in Audio-Language Evaluation
Abstract
Large Audio-Language Models show consistent performance gains across speech and audio benchmarks, yet high scores may not reflect true auditory perception. If a model can answer questions without processing the acoustic signal, the benchmark fails as a measure of auditory understanding. We present a diagnostic framework using two axes: text prior, which measures answerability from text and general knowledge alone, and audio reliance, which assesses actual dependency on the acoustic signal. Evaluating eight LALMs across three benchmarks, we find that models retain 60-72% of their full audio scores even without any audio input. Moreover, among items that require audio, only 3.0-4.2% need the complete audio clip; the majority can be resolved using localized fragments. These findings challenge the assumption that benchmark performance equals robust audio understanding, and we conclude with practical guidelines for improving evaluation reliability and benchmark design.
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 a critical analysis of the reliance on audio in audio-language models, challenging existing benchmarks and proposing a framework for better evaluation. The methodology and findings are significant, offering valuable insights for researchers and practitioners in the field of machine learning and audio understanding.
Methodology
The paper introduces a novel diagnostic framework that assesses audio-language models (LALMs) based on two axes: text prior and audio reliance. This dual-axis approach allows for a nuanced understanding of how much of a model's performance can be attributed to textual cues versus actual audio processing. The methodology is well-structured, employing controlled settings to quantify the text prior and audio reliance, which is a significant advancement in evaluating LALMs. The use of multiple benchmarks and a variety of models strengthens the robustness of the findings.
Results & evaluation
The experiments are thorough, evaluating eight LALMs across three distinct benchmarks. The results indicate a substantial grounding gap, revealing that models can achieve high scores without audio input, which challenges the assumption of robust auditory understanding. The analysis of performance retention with partial audio is particularly insightful, providing a clear picture of how audio information is utilized by the models. However, the paper could benefit from more detailed statistical analysis to support its claims.
Reproducibility
The paper provides a clear description of the experimental setup, including the models used and the evaluation protocols. However, it lacks specific URLs or repositories for code and data, which could hinder reproducibility. Including such resources would enhance the paper's impact and facilitate further research in this area.
Limitations
One limitation is the reliance on existing benchmarks, which may not fully capture the complexities of audio understanding. Additionally, while the study identifies issues with current benchmarks, it does not propose new benchmarks or datasets, which could be a missed opportunity for advancing the field. The findings may also be limited by the specific models and benchmarks chosen for evaluation.
Broader impact
The findings have significant implications for the design of future audio-language benchmarks and the evaluation of LALMs. By highlighting the potential for models to rely on textual priors rather than genuine auditory understanding, the paper calls for a reevaluation of how auditory capabilities are assessed in machine learning. This could lead to more accurate and reliable evaluations, ultimately improving the development of models that genuinely understand audio. The paper presents a critical analysis of the reliance on audio in audio-language models, challenging existing benchmarks and proposing a framework for better evaluation. The methodology and findings are significant, offering valuable insights for researchers and practitioners in the field of machine learning and audio understanding.
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