27 citations · 161 across the 39 of their papers we have counts for
7 papers · 1 filter
Flipping the Dialogue: Training and Evaluating User Language Models
Tarek Naous, Philippe Laban, Wei Xu +1
Conversations with LMs involve two participants: a human user leading the conversation, and an LM assistant responding to the user's request. To satisfy this specific role, LMs are…
Attribution Gradients: Incrementally Unfolding Citations for Critical Examination of Attributed AI Answers
Hita Kambhamettu, Alyssa Hwang, Philippe Laban +1
AI answer engines are a relatively new kind of information search tool: rather than returning a ranked list of documents, they generate an answer to a search question with inline c…
DeepTRACE: Auditing Deep Research AI Systems for Tracking Reliability Across Citations and Evidence
Pranav Narayanan Venkit, Philippe Laban, Yilun Zhou +3
Generative search engines and deep research LLM agents promise trustworthy, source-grounded synthesis, yet users regularly encounter overconfidence, weak sourcing, and confusing ci…
LLMs Get Lost In Multi-Turn Conversation
Philippe Laban, Hiroaki Hayashi, Yingbo Zhou +1
Large Language Models (LLMs) are conversational interfaces. As such, LLMs have the potential to assist their users not only when they can fully specify the task at hand, but also t…
AI-Slop to AI-Polish? Aligning Language Models through Edit-Based Writing Rewards and Test-time Computation
Tuhin Chakrabarty, Philippe Laban, Chien-Sheng Wu
AI-generated text is proliferating across domains, from creative writing and journalism to marketing content and scientific articles. Models can follow user-provided instructions t…
BingoGuard: LLM Content Moderation Tools with Risk Levels
Fan Yin, Philippe Laban, Xiangyu Peng +7
Malicious content generated by large language models (LLMs) can pose varying degrees of harm. Although existing LLM-based moderators can detect harmful content, they struggle to as…
Why Vision Language Models Struggle with Visual Arithmetic? Towards Enhanced Chart and Geometry Understanding
Kung-Hsiang Huang, Can Qin, Haoyi Qiu +4
Vision Language Models (VLMs) have achieved remarkable progress in multimodal tasks, yet they often struggle with visual arithmetic, seemingly simple capabilities like object count…