Postdoc, Hong Kong University of Science and Technology
1 paper at NeurIPS 2025
SemDiD generates semantically diverse LLM outputs by guiding decoding in embedding space through orthogonal direction vectors and inter-group repulsion, outperforming existing methods in Best-of-N evaluations and accelerating RLHF training.
