7 papers
Watermarking Diffusion Language Models with Learned Foresight and Balanced Bands
Hyojun Kim, Hyeseon An, Yo-Sub Han
Green-red watermarks favor tokens from a green list that a secret key derives from the preceding tokens. Diffusion language models, however, often choose a token before its precedi…
Grammar-Guided Code Watermarking with Green Temperature
Hyundong Jin, Hyeseon An, Soohan Lim +1
Large language model watermarking embeds detectable statistical signals during decoding, but the resulting changes to token probabilities can degrade generation quality. This trade…
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…
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…
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,…
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…