1 citations · 2 across the 11 of their papers we have counts for
11 papers
Linguistics-Aware Non-Distortionary LLM Watermarking
Shinwoo Park, Hyejin Park, Hyeseon An +1
Watermarking should identify language-model output without degrading quality or limiting verification to the model provider. Multilingual deployment makes this harder because morph…
Sequential Behavioral Watermarking for LLM Agents
Hyeseon An, Shinwoo Park, Dongsu Kim +1
LLM-based agents act through sequences of executable decisions, but their trajectories provide little evidence of which agent or policy produced them, making provenance, ownership,…
When LLM Essays Outscore Student Essays: What a Korean Writing Rubric Rewards and Where Readers Disagree
Shinwoo Park, Yo-Sub Han
LLMs now help students plan, draft, and revise essays. Educational assessment therefore faces a basic question: how should student and LLM writing be compared? Rubrics assign point…
Steering Language Models Before They Speak: Logit-Level Interventions
Hyeseon An, Shinwoo Park, Hyundong Jin +1
Controllable generation requires language models to realize output characteristics such as reading level, politeness, and toxicity. Existing steering methods are often indirect, re…
WaterMod: Modular Token-Rank Partitioning for Probability-Balanced LLM Watermarking
Shinwoo Park, Hyejin Park, Hyeseon Ahn +1
Large language models now draft news, legal analyses, and software code with human-level fluency. At the same time, regulations such as the EU AI Act mandate that each synthetic pa…
A Linguistics-Aware LLM Watermarking via Syntactic Predictability
Shinwoo Park, Hyejin Park, Hyeseon An +1
As large language models (LLMs) continue to advance rapidly, reliable governance tools have become critical. Publicly verifiable watermarking is particularly essential for fosterin…
DITTO: A Spoofing Attack Framework on Watermarked LLMs via Knowledge Distillation
Hyeseon An, Shinwoo Park, Suyeon Woo +1
The promise of LLM watermarking rests on a core assumption that a specific watermark proves authorship by a specific model. We demonstrate that this assumption is dangerously flawe…
TRAPDOC: Deceiving LLM Users by Injecting Imperceptible Phantom Tokens into Documents
Hyundong Jin, Sicheol Sung, Shinwoo Park +2
The reasoning, writing, text-editing, and retrieval capabilities of proprietary large language models (LLMs) have advanced rapidly, providing users with an ever-expanding set of fu…
KatFishNet: Detecting LLM-Generated Korean Text through Linguistic Feature Analysis
Shinwoo Park, Shubin Kim, Do-Kyung Kim +1
The rapid advancement of large language models (LLMs) increases the difficulty of distinguishing between human-written and LLM-generated text. Detecting LLM-generated text is cruci…
Marking Code Without Breaking It: Code Watermarking for Detecting LLM-Generated Code
Jungin Kim, Shinwoo Park, Yo-Sub Han
Identifying LLM-generated code through watermarking poses a challenge in preserving functional correctness. Previous methods rely on the assumption that watermarking high-entropy t…
Detection of LLM-Paraphrased Code and Identification of the Responsible LLM Using Coding Style Features
Shinwoo Park, Hyundong Jin, Jeong-won Cha +1
Recent progress in large language models (LLMs) for code generation has raised serious concerns about intellectual property protection. Malicious users can exploit LLMs to produce…