Author

Niklas Muennighoff

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#artificial intelligence Feb 2024

KTO: Model Alignment as Prospect Theoretic Optimization

This work proposes a HALO that directly maximizes the utility of generations instead of maximizing the log-likelihood of preferences, and suggests that the best loss depends on the inductive biases most appropriate for a given setting, an oft-overlooked consideration.

Kawin Ethayarajh, Winnie Xu, Niklas Muennighoff et al. · arXiv.org · 195 citations · ⚡28

AutoJudger : An Adaptive Evaluation Framework for Efficient Benchmarking of MLLMs

This work introduces A 2 -Judger, a novel MLLM-based A gentic instantiation of A uto Judger equipped with semantic-aware retrieval and dynamic memory that significantly improves sample efficiency while maintaining reliable evaluation results.

Xuanwen Ding, Chengjun Pan, Zejun Li et al. · 0 citations

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 · 5 citations