arXiv:2605.22170v1 Announce Type: new Abstract: In recent years, several Speech Language Models (SLMs) that represent speech and written text jointly have been presented. The question then emerges about how model-internal mechanisms are similar and different when operating in the two modalities. We focus on how these systems encode, store, and retrieve factual knowledge, which has previously been investigated for text-only models. To investigate mechanisms behind the storage and recall of factual association in SLMs, we leverage Causal Mediation Analysis, a technique previously applied to text
The proliferation of multimodal AI models necessitates a deeper understanding of their internal mechanisms to ensure reliability and advance capabilities, making this research timely.
Understanding how multimodal models encode factual knowledge is crucial for developing more robust, trustworthy, and generally intelligent AI systems, particularly for applications requiring high fidelity.
This research provides a methodology to investigate the consistency of factual recall mechanisms between text and speech in multimodal models, which could lead to improved model architectures and debugging techniques.
- · AI researchers
- · Multimodal AI developers
- · Speech technology companies
- · Developers of opaque black-box AI systems
Improved understanding of multimodal AI's internal workings for factual knowledge.
Development of more reliable and accurate Speech Language Models that consistently retrieve factual information.
Accelerated progress towards general AI systems that can seamlessly integrate and retrieve knowledge across diverse data modalities.
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