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Xinyu Hu

4 papers hereh-index 333 citations10 works total

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

author position
  • first author1
  • middle author2
  • last author1

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

fields
  • cs.CL2
  • cs.AI1
  • cs.CV1
same name
  • Xinyu Hu — 19 papers, h 6
  • Xinyu Hu — 10 papers, h 3
  • Xinyu Hu — 4 papers, h 5
  • Xinyu Hu — 3 papers, h 1
  • Xinyu Hu — 3 papers, h 1
  • Xinyu Hu — 2 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

activity
20232025
most citedEvoke: Evoking Critical Thinking Abilities in LLMs via Reviewer-Author Prompt Editing

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

collaborators

4 papers

cs.AI2025

WebGraphEval: Multi-Turn Trajectory Evaluation for Web Agents using Graph Representation

Yaoyao Qian, Yuanli Wang, Jinda Zhang +8

Current evaluation of web agents largely reduces to binary success metrics or conformity to a single reference trajectory, ignoring the structural diversity present in benchmark da…

cs.CV2024

What You See Is What Matters: A Novel Visual and Physics-Based Metric for Evaluating Video Generation Quality

Zihan Wang, Songlin Li, Lingyan Hao +2

As video generation models advance rapidly, assessing the quality of generated videos has become increasingly critical. Existing metrics, such as Fréchet Video Distance (FVD), Ince…

cs.CL2024

Task Oriented In-Domain Data Augmentation

Xiao Liang, Xinyu Hu, Simiao Zuo +5

Large Language Models (LLMs) have shown superior performance in various applications and fields. To achieve better performance on specialized domains such as law and advertisement,…

cs.CL2023★ 2 cited

Evoke: Evoking Critical Thinking Abilities in LLMs via Reviewer-Author Prompt Editing

Xinyu Hu, Pengfei Tang, Simiao Zuo +5

Large language models (LLMs) have made impressive progress in natural language processing. These models rely on proper human instructions (or prompts) to generate suitable response…

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