New framework reveals cross-modal computation in multilingual speech-text models

Researchers have developed a new framework to analyze how multilingual speech-text models handle cross-modal language alignment. This framework, applied to models like SeamlessM4T and Qwen2-Audio, identifies language-selective neurons and categorizes them into representation and control roles. The analysis revealed that SeamlessM4T shows significant generation-step-dependent specialization, with few shared neurons across modalities, while Qwen2-Audio maintains broader cross-modal sharing and stable alignment. The study also found that language-control neurons in SeamlessM4T increasingly transfer knowledge from speech to text during later generation steps. AI

IMPACT Provides new methods for understanding and potentially improving cross-modal alignment in multilingual AI systems.

RANK_REASON The cluster contains an academic paper detailing a new framework for analyzing AI models. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CL →

• arXiv
• Qwen2-Audio
• SeamlessM4T-Massively Multilingual & Multimodal Machine Translation
• Toshiki Nakai

• paper
• model release

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