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Dalian University of Technology
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Explicit semantic augmentation can boost LLM-based code translation success rates by over 200%, especially for smaller models.
LLMs can reason more efficiently by sharing intermediate thoughts during parallel search, achieving better accuracy with less computation.
Omni-modal LLMs can ace captioning and QA, but AVID reveals they're surprisingly bad at spotting audio-visual inconsistencies in videos, a crucial skill for trustworthy AI.
CLIP can now understand "no dog" without any fine-tuning, thanks to a plug-and-play module that disentangles negated semantics and penalizes false positive matches.
Key contribution not extracted.
