VIBE: Voice-Induced open-ended Bias Evaluation for Large Audio-Language Models via Real-World Speech
Abstract
Large Audio-Language Models (LALMs) are increasingly integrated into daily applications, yet their generative biases remain underexplored. Existing speech fairness benchmarks rely on synthetic speech and Multiple-Choice Questions (MCQs), both offering a fragmented view of fairness. We propose VIBE, a framework that evaluates generative bias through open-ended tasks such as personalized recommendations, using real-world human recordings. Unlike MCQs, our method allows stereotypical associations to manifest organically without predefined options, making it easily extensible to new tasks. Evaluating 11 state-of-the-art LALMs reveals systematic biases in realistic scenarios. We find that gender cues often trigger larger distributional shifts than accent cues, indicating that current LALMs reproduce social stereotypes.
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 VIBE, a novel framework for evaluating generative bias in large audio-language models through open-ended tasks using real-world speech. This innovative approach not only addresses a critical gap in current evaluation methods but also provides actionable insights into the biases present in LALMs, making it a significant contribution to the field of machine learning and audio processing.
Methodology
The proposed VIBE framework innovatively shifts the evaluation of generative biases in LALMs from traditional MCQ formats to open-ended tasks that allow for the organic emergence of biases. This approach is significant as it leverages real-world audio recordings, capturing a broader range of paralinguistic cues and phonetic variability. The methodology is well-structured, with a clear focus on extracting structured attributes from generated text, thus allowing for a quantifiable assessment of biases. The use of human-validated extraction methods adds robustness to the findings.
Results & evaluation
The experiments are comprehensive, evaluating 11 state-of-the-art LALMs across five diverse tasks that reflect realistic applications. The findings reveal systematic biases, particularly highlighting the stronger influence of gender cues over accent cues. The statistical rigor applied in measuring biases through total variation distance (TVD) and permutation tests strengthens the validity of the results. However, the paper could benefit from a more detailed discussion of the statistical methods used for bias quantification.
Reproducibility
The paper mentions the use of specific datasets and provides a URL for the evaluation prompts, which aids reproducibility. However, there is no explicit mention of the code or model weights being made available, which could hinder full reproducibility of the experiments. The authors should consider releasing their code and models to facilitate further research.
Limitations
The study is limited to English speech and does not account for spontaneous conversation, which may not fully represent natural vocal variation. Additionally, the definition of bias used may not encompass all aspects of fairness, such as individual fairness or intersectional bias. The reliance on specific datasets may also limit the generalizability of the findings to other languages or cultural contexts.
Broader impact
The implications of this research are significant for applications of LALMs in real-world scenarios, particularly in voice assistants and customer service, where biased outputs can perpetuate stereotypes. The framework provides a valuable tool for developers and researchers to audit and understand the biases in their models, potentially leading to more equitable AI systems. The focus on real-world speech recordings enhances the relevance of the findings to practical applications. The paper presents VIBE, a novel framework for evaluating generative bias in large audio-language models through open-ended tasks using real-world speech. This innovative approach not only addresses a critical gap in current evaluation methods but also provides actionable insights into the biases present in LALMs, making it a significant contribution to the field of machine learning and audio processing.
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