5 papers
Faster but Different: Diagnosing and Controlling Content Drift in Accelerated Multimodal Diffusion Language Models
Yaoxuan Dou, Yang Shu
Training-free acceleration makes diffusion-based multimodal large language models (dMLLMs) more deployable, but it may silently change generated content. We study this serving-time…
Gavel: Agent Meets Checklist for Evaluating LLMs on Long-Context Legal Summarization
Yao Dou, Benjamin Mamut, Wei Xu
Large language models (LLMs) now support contexts of up to 1M tokens, but their strengths and weaknesses on complex long-context tasks remain unclear. To study this, we focus on mu…
Localizing Prompt Ambiguity in Large Language Models with Probe-Targeted Attribution
Govind Ramesh, Yao Dou, Wei Xu
Prompt ambiguity is a common source of failure in large language models, but is difficult to localize because it is a latent property of the prompt, while existing attribution meth…
Evaluating LLMs on Chinese Idiom Translation
Cai Yang, Yao Dou, David Heineman +2
Idioms, whose figurative meanings usually differ from their literal interpretations, are common in everyday language, especially in Chinese, where they often contain historical ref…
Measuring, Modeling, and Helping People Account for Privacy Risks in Online Self-Disclosures with AI
Isadora Krsek, Anubha Kabra, Yao Dou +5
In pseudonymous online fora like Reddit, the benefits of self-disclosure are often apparent to users (e.g., I can vent about my in-laws to understanding strangers), but the privacy…