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researcher

Nan Yang

21 papers hereh-index 121.9k citations22 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • middle author21

Across the 21 of 21 papers where every author was matched, so the position is known.

fields
  • cs.CL14
  • cs.CV3
  • cs.IR3
  • cs.AI1
same name
  • Nan Yang — 55 papers, h 44
  • Nan Yang — 22 papers, h 31
  • Nan Yang — 17 papers, h 13
  • Nan Yang — 9 papers, h 3
  • Nan Yang — 9 papers, h 5
  • Nan Yang — 7 papers, h 1

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

activity
20232026
most citedMultilingual E5 Text Embeddings: A Technical Report

39 citations · 82 across the 19 of their papers we have counts for

collaborators
Showing 2026 · cs.CLShow all

4 papers · 2 filters

cs.CL2026

BitNet Text Embeddings

Zhen Li, Xin Huang, Liang Wang +8

LLM-based text embedders have substantially improved retrieval and semantic representation quality, but their deployment remains costly: large backbone models slow down embedding i…

cs.CL2026

Only Say What You Know: Calibration-Aware Generation for Long-Form Factuality

Wen Luo, Guangyue Peng, Liang Wang +7

Large Reasoning Models achieve strong performance on complex tasks but remain prone to hallucinations, particularly in long-form generation where errors compound across reasoning s…

cs.CL2026

Learning to Draft: Adaptive Speculative Decoding with Reinforcement Learning

Jiebin Zhang, Zhenghan Yu, Liang Wang +8

Speculative decoding accelerates large language model (LLM) inference by using a small draft model to generate candidate tokens for a larger target model to verify. The efficacy of…

cs.CL2026

Two Pathways to Truthfulness: On the Intrinsic Encoding of LLM Hallucinations

Wen Luo, Guangyue Peng, Wei Li +8

Despite their impressive capabilities, large language models (LLMs) frequently generate hallucinations. Previous work shows that their internal states encode rich signals of truthf…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.