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Chaoning Zhang

28 papers hereh-index 7145 citations49 works total

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

author position
  • first author1
  • middle author18
  • last author7

Across the 26 of 28 papers where every author was matched, so the position is known.

fields
  • cs.AI7
  • cs.CV7
  • cs.CL6
  • cs.LG4
  • cs.IR2
  • cs.MA1
same name
  • Chaoning Zhang — 53 papers, h 5
  • Chaoning Zhang — 21 papers, h 33
  • Chaoning Zhang — 4 papers, h 3
  • Chaoning Zhang — 2 papers, h 7
  • Chaoning Zhang — 1 paper
  • Chaoning Zhang — 1 paper

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
20242026
most citedBeyond Performance: Quantifying and Mitigating Label Bias in LLMs

1 citations · 1 across the 28 of their papers we have counts for

collaborators
Showing 2025Show all

4 papers · 1 filter

cs.CV2025

Fast SAM2 with Text-Driven Token Pruning

Avilasha Mandal, Chaoning Zhang, Fachrina Dewi Puspitasari +6

Segment Anything Model 2 (SAM2), a vision foundation model has significantly advanced in prompt-driven video object segmentation, yet their practical deployment remains limited by…

cs.AI2025

Understanding Chain-of-Thought in Large Language Models via Topological Data Analysis

Chenghao Li, Chaoning Zhang, Yi Lu +10

With the development of large language models (LLMs), particularly with the introduction of the long reasoning chain technique, the reasoning ability of LLMs in complex problem-sol…

cs.IR2025

Beyond Whole Dialogue Modeling: Contextual Disentanglement for Conversational Recommendation

Guojia An, Jie Zou, Jiwei Wei +3

Conversational recommender systems aim to provide personalized recommendations by analyzing and utilizing contextual information related to dialogue. However, existing methods typi…

cs.CL2025

Syzygy of Thoughts: Improving LLM CoT with the Minimal Free Resolution

Chenghao Li, Chaoning Zhang, Yi Lu +7

Chain-of-Thought (CoT) prompting enhances the reasoning of large language models (LLMs) by decomposing problems into sequential steps, mimicking human logic and reducing errors. Ho…

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