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Joonwon Jang

7 papers hereh-index 556 citations8 works total

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

author position
  • first author2
  • middle author5

Across the 7 of 7 papers where every author was matched, so the position is known.

fields
  • cs.CL4
  • cs.SE2
  • cs.AI1
same name
  • Joonwon Jang — 1 paper, h 3

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 citedRectifying Demonstration Shortcut in In-Context Learning

1 citations · 1 across the 7 of their papers we have counts for

collaborators
Showing cs.CLShow all

4 papers · 1 filter

cs.CL2025

EXAONE 4.0: Unified Large Language Models Integrating Non-reasoning and Reasoning Modes

Kyunghoon Bae, Eunbi Choi, Kibong Choi +37

This technical report introduces EXAONE 4.0, which integrates a Non-reasoning mode and a Reasoning mode to achieve both the excellent usability of EXAONE 3.5 and the advanced reaso…

cs.CL2025

Are Vision-Language Models Safe in the Wild? A Meme-Based Benchmark Study

DongGeon Lee, Joonwon Jang, Jihae Jeong +1

Rapid deployment of vision-language models (VLMs) magnifies safety risks, yet most evaluations rely on artificial images. This study asks: How safe are current VLMs when confronted…

cs.CL2024

Verbosity-Aware Rationale Reduction: Effective Reduction of Redundant Rationale via Principled Criteria

Joonwon Jang, Jaehee Kim, Wonbin Kweon +2

Large Language Models (LLMs) rely on generating extensive intermediate reasoning units (e.g., tokens, sentences) to enhance final answer quality across a wide range of complex task…

cs.CL2024★ 1 cited

Rectifying Demonstration Shortcut in In-Context Learning

Joonwon Jang, Sanghwan Jang, Wonbin Kweon +2

Large language models (LLMs) are able to solve various tasks with only a few demonstrations utilizing their in-context learning (ICL) abilities. However, LLMs often rely on their p…

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