2 citations · 4 across the 33 of their papers we have counts for
14 papers · 1 filter
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…
EPIC: Efficient and Parallel Inference under CFG Constraints for Diffusion Language Models
Hyundong Jin, Yo-Sub Han
Controlling language model outputs is essential for ensuring structural validity, reliability, and downstream usability, and diffusion language models are no exception. Recent adva…
DLM-SWAI: Steering Diffusion Language Models Before They Unmask
Hyeseon An, Yo-Sub Han
Steering language model generation toward desired textual properties is essential for practical deployment, and inference-time methods are particularly appealing because they enabl…
Adaptive Steering and Remasking for Safe Generation in Diffusion Language Models
Yejin Lee, Yo-Sub Han, Ungsik Kim
Diffusion Language Models(DLMs) provide a promising alternative to autoregressive language models through iterative denoising and bidirectional generation. However, their iterative…
NCO: A Versatile Plug-in for Handling Negative Constraints in Decoding
Hyundong Jin, Yo-Sub Han
Controlling Large Language Models (LLMs) to prevent the generation of undesirable content, such as profanity and personally identifiable information (PII), has become increasingly…
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…
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…
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…
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…
A Framework for Quantum Finite-State Languages with Density Mapping
SeungYeop Baik, Sicheol Sung, Yo-Sub Han
A quantum finite-state automaton (QFA) is a theoretical model designed to simulate the evolution of a quantum system with finite memory in response to sequential input strings. We…
ATHENA: Mathematical Reasoning with Thought Expansion
JB. Kim, Hazel Kim, Joonghyuk Hahn +1
Solving math word problems depends on how to articulate the problems, the lens through which models view human linguistic expressions. Real-world settings count on such a method ev…