Do Audio-Visual Large Language Models Really See and Hear?
Abstract
Audio-Visual Large Language Models (AVLLMs) are emerging as unified interfaces to multimodal perception. We present the first mechanistic interpretability study of AVLLMs, analyzing how audio and visual features evolve and fuse through different layers of an AVLLM to produce the final text outputs. We find that although AVLLMs encode rich audio semantics at intermediate layers, these capabilities largely fail to surface in the final text generation when audio conflicts with vision. Probing analyses show that useful latent audio information is present, but deeper fusion layers disproportionately privilege visual representations that tend to suppress audio cues. We further trace this imbalance to training: the AVLLM's audio behavior strongly matches its vision-language base model, indicating limited additional alignment to audio supervision. Our findings reveal a fundamental modality bias in AVLLMs and provide new mechanistic insights into how multimodal LLMs integrate audio and vision.
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 main contribution of this paper lies in its exploration of the mechanistic interpretability of AVLLMs, revealing a fundamental modality bias that could inform future research and model design. The study's findings underscore the importance of addressing modality imbalance in multimodal systems, which is a critical area for advancing the field of machine learning.
Methodology
The paper presents a mechanistic interpretability study of Audio-Visual Large Language Models (AVLLMs), focusing on how audio and visual features are integrated at various layers of the model. The methodology includes probing analyses to assess the presence of audio semantics and their influence on text generation. This approach is valuable as it provides insights into the inner workings of AVLLMs, although it could benefit from a more detailed description of the probing techniques and the specific metrics used to evaluate audio and visual feature integration.
Results & evaluation
The experiments conducted reveal a significant finding regarding the suppression of audio cues in favor of visual representations in AVLLMs. However, the paper lacks a comprehensive dataset description and does not provide quantitative results that could substantiate the claims made. The absence of comparative benchmarks against other models or methods limits the ability to gauge the performance and relevance of the findings.
Reproducibility
The paper does not provide sufficient implementation details or code availability, which raises concerns about reproducibility. The lack of a clear methodology for replicating the experiments or accessing the datasets used makes it challenging for other researchers to validate the findings.
Limitations
One significant limitation is the focus on the imbalance between audio and visual modalities without exploring potential solutions or improvements to mitigate this bias. Additionally, the findings are primarily qualitative, which may not fully capture the nuances of AVLLMs' performance in real-world applications.
Broader impact
The insights gained from this study could have implications for the development of more balanced multimodal models that effectively integrate audio and visual information. This could enhance applications in various fields, including robotics, multimedia content generation, and human-computer interaction, where understanding and processing multiple modalities are crucial. The main contribution of this paper lies in its exploration of the mechanistic interpretability of AVLLMs, revealing a fundamental modality bias that could inform future research and model design. The study's findings underscore the importance of addressing modality imbalance in multimodal systems, which is a critical area for advancing the field of machine learning.
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