3 papers
cs.CL2026
SemPA: Improving Sentence Embeddings of Large Language Models through Semantic Preference Alignment
Ziyang Chen, Zhenxuan Huang, Yile Wang +3
Traditional sentence embedding methods employ token-level contrastive learning on non-generative pre-trained models. Recently, there have emerged embedding methods based on generat…
cs.CL2025
Ranked Voting based Self-Consistency of Large Language Models
Weiqin Wang, Yile Wang, Hui Huang
Majority voting is considered an effective method to enhance chain-of-thought reasoning, as it selects the answer with the highest "self-consistency" among different reasoning path…
cs.CL2025
LDIR: Low-Dimensional Dense and Interpretable Text Embeddings with Relative Representations
Yile Wang, Zhanyu Shen, Hui Huang
Semantic text representation is a fundamental task in the field of natural language processing. Existing text embedding (e.g., SimCSE and LLM2Vec) have demonstrated excellent perfo…