2 citations · 4 across the 33 of their papers we have counts for
16 papers · 1 filter
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…
ECO: Enhanced Code Optimization via Performance-Aware Prompting for Code-LLMs
Su-Hyeon Kim, Joonghyuk Hahn, Sooyoung Cha +1
Code runtime optimization-the task of rewriting a given code to a faster one-remains challenging, as it requires reasoning about performance trade-offs involving algorithmic and st…
RegexPSPACE: A Benchmark for Evaluating LLM Reasoning on PSPACE-complete Regex Problems
Hyundong Jin, Joonghyuk Hahn, Yo-Sub Han
Large language models (LLMs) show strong performance across natural language processing (NLP), mathematical reasoning, and programming, and recent large reasoning models (LRMs) fur…
MECO: Multi-Expert Consensus for Code Time Complexity Prediction
Joonghyuk Hahn, Soohan Lim, Yo-Sub Han
Predicting the complexity of source code is essential for software development and algorithm analysis. Recently, Baik et al. (2025) introduced CodeComplex for code time complexity…
Repairing Regex Vulnerabilities via Localization-Guided Instructions
Sicheol Sung, Joonghyuk Hahn, Yo-Sub Han
Regular expressions (regexes) are foundational to modern computing for critical tasks like input validation and data parsing, yet their ubiquity exposes systems to regular expressi…
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…
ContractEval: A Benchmark for Evaluating Contract-Satisfying Assertions in Code Generation
Soohan Lim, Joonghyuk Hahn, Hyunwoo Park +2
Current code generation evaluation measures functional correctness on well-formed inputs that satisfy all input preconditions. This paradigm has a critical limitation: task descrip…
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…
RV-HATE: Reinforced Multi-Module Voting for Implicit Hate Speech Detection
Yejin Lee, Hyeseon Ahn, Yo-Sub Han
Hate speech remains prevalent in human society and continues to evolve in its forms and expressions. Modern advancements in internet and online anonymity accelerate its rapid sprea…
Obfuscation Rules for Detecting and Detoxifying Korean Toxicity
Yejin Lee, Su-Hyeon Kim, Hyundong Jin +3
As language models become increasingly deployed in online environments, toxicity detection and detoxification have received growing attention. Existing studies primarily focus on n…
AmpleHate: Amplifying the Attention for Versatile Implicit Hate Detection
Yejin Lee, Joonghyuk Hahn, Hyeseon Ahn +1
Implicit hate speech detection is challenging due to its subtlety and reliance on contextual interpretation rather than explicit offensive words. Current approaches rely on contras…
TCProF: Time-Complexity Prediction SSL Framework
Joonghyuk Hahn, Hyeseon Ahn, Jungin Kim +2
Time complexity is a theoretic measure to determine the amount of time the algorithm needs for its execution. In reality, developers write algorithms into code snippets within limi…
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…
URECA: The Chain of Two Minimum Set Cover Problems exists behind Adaptation to Shifts in Semantic Code Search
Seok-Ung Choi, Joonghyuk Hahn, Yo-Sub Han
Adaptation is to make model learn the patterns shifted from the training distribution. In general, this adaptation is formulated as the minimum entropy problem. However, the minimu…
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…