arXiv:2509.22363v4 Announce Type: replace Abstract: Large Audio Language Models (LALMs) integrate audio encoders with pretrained Large Language Models to perform complex multimodal reasoning tasks. While these models can generate Chain-of-Thought (CoT) explanations, the faithfulness of these reasoning chains remains unclear. In this work, we propose a systematic framework to evaluate CoT faithfulness in LALMs with respect to both the input audio and the final model prediction. We define three criteria for audio faithfulness: hallucination-free, holistic, and attentive listening. We also introd
The rapid development and deployment of LLMs into multimodal domains necessitates research into their reliability and interpretability, especially as they integrate with new data types like audio.
As Large Audio Language Models (LALMs) become more sophisticated, understanding their faithfulness is crucial for trustworthy AI systems capable of complex multimodal reasoning, impacting adoption and regulation.
This research provides a framework for evaluating the trustworthiness of LALMs, which could lead to more robust and explainable AI applications in diverse audio-driven tasks.
- · AI developers
- · Generative AI startups
- · AI ethics and safety researchers
- · Multimodal AI applications
- · AI systems lacking interpretability
- · Applications with high-stakes audio reasoning
- · Companies neglecting AI faithfulness research
Improved reliability and explainability of multimodal AI systems, particularly those incorporating audio.
Increased user trust and broader adoption of AI in domains requiring audio understanding and reasoning.
New regulatory frameworks and industry standards specifically addressing faithfulness in multimodal AI applications, potentially impacting market access.
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