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Taesup Kim

11 papers hereh-index 216 citations11 works total

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

author position
  • middle author2
  • last author9

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

fields
  • cs.CL4
  • cs.AI2
  • cs.LG2
  • cs.CR1
  • cs.IR1
  • cs.SD1
same name
  • Taesup Kim — 16 papers, h 14
  • Taesup Kim — 10 papers, h 2
  • Taesup Kim — 7 papers, h 2
  • Taesup Kim — 6 papers, h 1
  • Taesup Kim — 5 papers, h 1
  • Taesup Kim — 4 papers, h 2

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 citedMitigating Hallucination in Abstractive Summarization with Domain-Conditional Mutual Information

3 citations · 3 across the 10 of their papers we have counts for

collaborators
Showing 2025Show all

4 papers · 1 filter

cs.LG2025

Angular Gradient Sign Method: Uncovering Vulnerabilities in Hyperbolic Networks

Minsoo Jo, Dongyoon Yang, Taesup Kim

Adversarial examples in neural networks have been extensively studied in Euclidean geometry, but recent advances in \textit{hyperbolic networks} call for a reevaluation of attack s…

cs.CL2025

KoSimpleQA: A Korean Factuality Benchmark with an Analysis of Reasoning LLMs

Donghyeon Ko, Yeguk Jin, Kyubyung Chae +6

We present Korean SimpleQA (KoSimpleQA), a benchmark for evaluating factuality in large language models (LLMs) with a focus on Korean cultural knowledge. KoSimpleQA is d…

cs.CL2025

Assessing Socio-Cultural Alignment and Technical Safety of Sovereign LLMs

Kyubyung Chae, Gihoon Kim, Gyuseong Lee +3

Recent trends in LLMs development clearly show growing interest in the use and application of sovereign LLMs. The global debate over sovereign LLMs highlights the need for governme…

cs.CR2025

From Threat to Tool: Leveraging Refusal-Aware Injection Attacks for Safety Alignment

Kyubyung Chae, Hyunbin Jin, Taesup Kim

Safely aligning large language models (LLMs) often demands extensive human-labeled preference data, a process that's both costly and time-consuming. While synthetic data offers a p…

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