Researchers have developed SEATS, a new method to make omni-modal large language models (om-LLMs) more efficient. SEATS prunes redundant audio-visual tokens throughout the model's layers, adapting the token selection process based on cross-modal fusion. This approach significantly reduces computational load and speeds up inference while maintaining high performance. AI
影响 Reduces computational overhead and speeds up inference for multi-modal LLMs, potentially lowering deployment costs.
排序理由 The cluster contains a research paper detailing a new method for improving LLM efficiency.
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