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Chen Zhang

11 papers hereh-index 4153 citations11 works total

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

author position
  • first author2
  • middle author6
  • last author1

Across the 9 of 11 papers where every author was matched, so the position is known.

fields
  • cs.AI4
  • cs.CL4
  • cs.IR2
  • cs.LG1
same name
  • Chen Zhang — 18 papers, h 10
  • Chen Zhang — 16 papers, h 12
  • Chen Zhang — 15 papers, h 10
  • Chen Zhang — 12 papers, h 2
  • Chen Zhang — 11 papers, h 4
  • Chen Zhang — 11 papers, h 5

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 citedA Survey on Multi-Turn Interaction Capabilities of Large Language Models

2 citations · 4 across the 9 of their papers we have counts for

collaborators
Showing cs.CLShow all

4 papers · 1 filter

cs.CL2026

InsightChain: Optimized Chain-of-Insight Analytics for LLM-driven Data Visualization

Hanya Sun, Chen Zhang, Sheng Liang +2

Large language models (LLMs) are increasingly used for automated data visualization, yet existing approaches often frame visualization generation as a single-step mapping from user…

cs.CL2026

Deep-Reporter: Deep Research for Grounded Multimodal Long-Form Generation

Fangda Ye, Zhifei Xie, Yuxin Hu +5

Recent agentic search frameworks enable deep research via iterative planning and retrieval, reducing hallucinations and enhancing factual grounding. However, they remain text-centr…

cs.CL2026

SRR-Judge: Step-Level Rating and Refinement for Enhancing Search-Integrated Reasoning in Search Agents

Chen Zhang, Kuicai Dong, Dexun Li +4

Recent deep search agents built on large reasoning models (LRMs) excel at complex question answering by iteratively planning, acting, and gathering evidence, a capability known as…

cs.CL2025★ 2 cited

A Survey on Multi-Turn Interaction Capabilities of Large Language Models

Chen Zhang, Xinyi Dai, Yaxiong Wu +4

Multi-turn interaction in the dialogue system research refers to a system's ability to maintain context across multiple dialogue turns, enabling it to generate coherent and context…

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