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Min Zhang

4 papers here

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

author position
  • middle author1
  • last author3

Across the 4 of 4 papers where every author was matched, so the position is known.

fields
  • cs.CL3
  • cs.IR1
ORCID 0009-0003-4063-0082
same name
  • Min Zhang — 44 papers
  • Min Zhang — 19 papers, h 26
  • Min Zhang — 17 papers
  • Min Zhang — 14 papers
  • Min Zhang — 12 papers, h 7
  • Min Zhang — 9 papers, h 49

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

most citedSequential Recommendation with Latent Relations based on Large Language Model

3 citations · 4 across the 4 of their papers we have counts for

collaborators

4 papers

cs.CL2025

SynDec: A Synthesize-then-Decode Approach for Arbitrary Textual Style Transfer via Large Language Models

Han Sun, Zhen Sun, Zongmin Zhang +3

Large Language Models (LLMs) are emerging as dominant forces for textual style transfer. However, for arbitrary style transfer, LLMs face two key challenges: (1) considerable relia…

cs.CL2024

Translatotron-V(ison): An End-to-End Model for In-Image Machine Translation

Zhibin Lan, Liqiang Niu, Fandong Meng +3

In-image machine translation (IIMT) aims to translate an image containing texts in source language into an image containing translations in target language. In this regard, convent…

cs.CL2024★ 1 cited

Improving Attributed Text Generation of Large Language Models via Preference Learning

Dongfang Li, Zetian Sun, Baotian Hu +4

Large language models have been widely adopted in natural language processing, yet they face the challenge of generating unreliable content. Recent works aim to reduce misinformati…

cs.IR2024★ 3 cited

Sequential Recommendation with Latent Relations based on Large Language Model

Shenghao Yang, Weizhi Ma, Peijie Sun +4

Sequential recommender systems predict items that may interest users by modeling their preferences based on historical interactions. Traditional sequential recommendation methods r…

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