AR&D: A Framework for Retrieving and Describing Concepts for Interpreting AudioLLMs
Abstract
Despite strong performance in audio perception tasks, large audio-language models (AudioLLMs) remain opaque to interpretation. A major factor behind this lack of interpretability is that individual neurons in these models frequently activate in response to several unrelated concepts. We introduce the first mechanistic interpretability framework for AudioLLMs, leveraging sparse autoencoders (SAEs) to disentangle polysemantic activations into monosemantic features. Our pipeline identifies representative audio clips, assigns meaningful names via automated captioning, and validates concepts through human evaluation and steering. Experiments show that AudioLLMs encode structured and interpretable features, enhancing transparency and control. This work provides a foundation for trustworthy deployment in high-stakes domains and enables future extensions to larger models, multilingual audio, and more fine-grained paralinguistic features. Project URL: https://townim-faisal.github.io/AutoInterpret-AudioLLM/
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 AR&D, a pioneering framework for interpreting AudioLLMs by disentangling polysemantic activations into interpretable features, significantly advancing the field of audio machine learning. The methodology is innovative and well-executed, demonstrating substantial technical impact and relevance in enhancing model interpretability.
Methodology
The paper introduces the AR&D framework, which effectively utilizes sparse autoencoders to disentangle polysemantic activations in AudioLLMs. The methodology is well-structured, comprising three main components: feature disentanglement, representative audio retrieval, and interpretable concept naming. The use of human evaluation to validate the concepts adds robustness to the approach. However, the reliance on automated captioning for naming may introduce biases or inaccuracies, which should be addressed in future work.
Results & evaluation
The experiments are comprehensive, comparing AR&D against multiple baseline methods. The metrics used for evaluation, including precision, recall, and F1 scores, are appropriate for assessing interpretability. The results demonstrate a clear advantage of AR&D over the baselines, with significant improvements in semantic alignment and steering sensitivity. However, the paper could benefit from more extensive ablation studies to further validate the contributions of each component in the pipeline.
Reproducibility
The implementation details provided are thorough, including specifics on datasets, training parameters, and model architectures. However, the paper lacks a complete code release or clear instructions for reproducing the experiments, which could hinder reproducibility efforts by other researchers.
Limitations
One limitation is the potential bias in automated captioning, which may not always accurately reflect human perceptions of audio concepts. Additionally, the framework's performance may vary with different AudioLLMs, and the scalability to larger models or diverse datasets remains to be fully explored.
Broader impact
The proposed framework has significant implications for enhancing the interpretability of AudioLLMs, which is crucial for their deployment in high-stakes applications such as healthcare and assistive technologies. By improving transparency, AR&D can foster trust in AI systems that rely on audio data, paving the way for more responsible and ethical AI practices. The paper presents AR&D, a pioneering framework for interpreting AudioLLMs by disentangling polysemantic activations into interpretable features, significantly advancing the field of audio machine learning. The methodology is innovative and well-executed, demonstrating substantial technical impact and relevance in enhancing model interpretability.
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