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Jeonghoon Kim

9 papers hereh-index 576 citations11 works total

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

author position
  • first author1
  • middle author6
  • last author1

Across the 8 of 9 papers where every author was matched, so the position is known.

fields
  • cs.CL6
  • cs.LG3
same name
  • Jeonghoon Kim — 5 papers, h 8
  • Jeonghoon Kim — 3 papers, h 1
  • Jeonghoon Kim — 2 papers, h 1
  • Jeonghoon Kim — 2 papers, h 2
  • Jeonghoon Kim — 1 paper
  • Jeonghoon Kim — 1 paper, h 4

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 citedHyperCLOVA X Technical Report

7 citations · 7 across the 9 of their papers we have counts for

collaborators
Showing 2025Show all

4 papers · 1 filter

cs.CL2025

Enhancing Hallucination Detection via Future Context

Joosung Lee, Cheonbok Park, Hwiyeol Jo +3

Large Language Models (LLMs) are widely used to generate plausible text on online platforms, without revealing the generation process. As users increasingly encounter such black-bo…

cs.CL2025

Cross-lingual Collapse: How Language-Centric Foundation Models Shape Reasoning in Large Language Models

Cheonbok Park, Jeonghoon Kim, Joosung Lee +3

Reinforcement learning with verifiable reward (RLVR) has been instrumental in eliciting strong reasoning capabilities from large language models (LLMs) via long chains of thought (…

cs.LG2025

ReGUIDE: Data Efficient GUI Grounding via Spatial Reasoning and Search

Hyunseok Lee, Jeonghoon Kim, Beomjun Kim +7

Recent advances in Multimodal Large Language Models (MLLMs) have enabled autonomous agents to interact with computers via Graphical User Interfaces (GUIs), where accurately localiz…

cs.LG2025

Peri-LN: Revisiting Normalization Layer in the Transformer Architecture

Jeonghoon Kim, Byeongchan Lee, Cheonbok Park +7

Selecting a layer normalization (LN) strategy that stabilizes training and speeds convergence in Transformers remains difficult, even for today's large language models (LLM). We pr…

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