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researcher

Xianjun Yang

UCSB

36 papers hereh-index 221.9k citations42 works total

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

author position
  • first author14
  • middle author21

Across the 35 of 36 papers where every author was matched, so the position is known.

fields
  • cs.CL27
  • cs.AI3
  • cs.CV2
  • cs.CR1
  • cs.CY1
  • cs.LG1
affiliations
  • UCSB
same name
  • Xianjun Yang — 3 papers, h 3
  • Xianjun Yang — 2 papers, h 3
  • Xianjun Yang — 2 papers, h 3
  • Xianjun Yang — 1 paper, h 9
  • Xianjun Yang — 1 paper, h 2
  • Xianjun Yang — 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
20212026
most citedExploring the Limits of ChatGPT for Query or Aspect-based Text Summarization

89 citations · 243 across the 36 of their papers we have counts for

collaborators
Showing 2025 · cs.CLShow all

4 papers · 2 filters

cs.CL2025

Verifying Chain-of-Thought Reasoning via Its Computational Graph

Zheng Zhao, Yeskendir Koishekenov, Xianjun Yang +2

Current Chain-of-Thought (CoT) verification methods predict reasoning correctness based on outputs (black-box) or activations (gray-box), but offer limited insight into why a compu…

cs.CL2025

Your thoughts tell who you are: Characterize the reasoning patterns of LRMs

Yida Chen, Yuning Mao, Xianjun Yang +7

Current comparisons of large reasoning models (LRMs) focus on macro-level statistics such as task accuracy or reasoning length. Whether different LRMs reason differently remains an…

cs.CL2025

Many-Turn Jailbreaking

Xianjun Yang, Liqiang Xiao, Shiyang Li +5

Current jailbreaking work on large language models (LLMs) aims to elicit unsafe outputs from given prompts. However, it only focuses on single-turn jailbreaking targeting one speci…

cs.CL2025

Diversity-driven Data Selection for Language Model Tuning through Sparse Autoencoder

Xianjun Yang, Shaoliang Nie, Lijuan Liu +5

Instruction tuning data are often quantity-saturated due to the large volume of data collection and fast model iteration, leaving data selection important but underexplored. Existi…

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