Researchers map retrieval heads across audio, vision, language modalities
A new study of 18 multimodal model checkpoints reveals that retrieval heads, the sparse attention mechanisms that locate evidence in long inputs, remain consistent across audio, vision, and language, opening new
Researchers studying multimodal large language models have identified how the sparse attention heads responsible for retrieving relevant information from long contexts behave consistently across different input types, from text and images to audio. The analysis of 18 language, vision-language, and o...
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- Source type
- Primary publication (lab/vendor blog) — our analysis + implication
- Source link
- r/arxiv-compression
- Published
- UTC
- Byline
- By the gotcontext.ai team (editorial standards)
- Correction?
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