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MetaEncoder

Meta AI researchers fine-tuned a 30-billion-parameter multimodal model into a bi-encoder that uses natural language to evaluate both small decision sets and massive retrieval pools.

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What it does

The paper covers small answer sets as well as retrieval among millions of candidates, with images and video included. Putting options in the request helps on some tasks, but reasoning is still a bottleneck. Its text-only JevBench results sit slightly below Open-Jev-27B. No released MetaEncoder weights are identified.

Maker-reported (not independently measured by JevMade): Jianpeng Cheng and colleagues reports: 0.7387 accuracy on JevBench hard split · Jianpeng Cheng and colleagues reports: 0.9444 accuracy on JevBench original split · Jianpeng Cheng and colleagues reports: 0.8555 accuracy on ImaJev dev split

Primitives
choice, score, noul
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