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Zheng Zhang

4 papers hereh-index 270 citations5 works total

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

author position
  • middle author3
  • last author1

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

fields
  • cs.CL3
  • cs.CV1
same name
  • Zheng Zhang — 47 papers, h 47
  • Zheng Zhang — 23 papers, h 31
  • Zheng Zhang — 21 papers, h 28
  • Zheng Zhang — 17 papers
  • Zheng Zhang — 16 papers, h 9
  • Zheng Zhang — 15 papers, h 43

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 citedRAGChecker: A Fine-grained Framework for Diagnosing Retrieval-Augmented Generation

9 citations · 9 across the 3 of their papers we have counts for

collaborators

4 papers

cs.CL2026

IRIS: Interleaved Reinforcement with Incremental Staged Curriculum for Cross-Lingual Mathematical Reasoning

Navya Gupta, Rishitej Reddy Vyalla, Avinash Anand +8

Curriculum learning helps language models tackle complex reasoning by gradually increasing task difficulty. However, it often fails to generate consistent step-by-step reasoning, e…

cs.CL2026

Large Language Models Explore by Latent Distilling

Yuanhao Zeng, Ao Lu, Lufei Li +3

Generating diverse responses is crucial for test-time scaling of large language models (LLMs), yet standard stochastic sampling mostly yields surface-level lexical variation, limit…

cs.CL2024★ 9 cited

RAGChecker: A Fine-grained Framework for Diagnosing Retrieval-Augmented Generation

Dongyu Ru, Lin Qiu, Xiangkun Hu +15

Despite Retrieval-Augmented Generation (RAG) showing promising capability in leveraging external knowledge, a comprehensive evaluation of RAG systems is still challenging due to th…

cs.CV2024

Unified Lexical Representation for Interpretable Visual-Language Alignment

Yifan Li, Yikai Wang, Yanwei Fu +3

Visual-Language Alignment (VLA) has gained a lot of attention since CLIP's groundbreaking work. Although CLIP performs well, the typical direct latent feature alignment lacks clari…

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