AutoJudger : An Adaptive Evaluation Framework for Efficient Benchmarking of MLLMs
Xuanwen Ding, Chengjun Pan, Zejun Li et al.
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Preprint
Aug 2026
The Embedder's Dilemma: LLMs Are Better, but at What Cost?
These results support a division of labour: use embedding models for similarity, classification, and clustering, and reserve LLMs for reasoning-intensive retrieval, and reserve LLMs for reasoning-intensive retrieval.
Adnan El Assadi, Niklas Muennighoff, Jinhyuk Lee
· 1 citation