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SEATS method slashes LLM compute by pruning audio-visual tokens

By PulseAugur Editorial · [2 sources] ·

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

IMPACT Reduces computational overhead and speeds up inference for multi-modal LLMs, potentially lowering deployment costs.

RANK_REASON The cluster contains a research paper detailing a new method for improving LLM efficiency.

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AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

SEATS method slashes LLM compute by pruning audio-visual tokens

COVERAGE [2]

  1. Hugging Face Daily Papers TIER_1 English(EN) ·

    Stage-adaptive Token Selection for Efficient Omni-modal LLMs

    Omni-modal large language models (om-LLMs) achieve unified audio-visual understanding by encoding video and audio into temporally aligned token sequences interleaved at the window level. However, processing these dense non-textual tokens throughout the LLM incurs substantial comp…

  2. arXiv cs.CV TIER_1 English(EN) · Xirong Li ·

    Stage-adaptive Token Selection for Efficient Omni-modal LLMs

    Omni-modal large language models (om-LLMs) achieve unified audio-visual understanding by encoding video and audio into temporally aligned token sequences interleaved at the window level. However, processing these dense non-textual tokens throughout the LLM incurs substantial comp…

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