8 citations · 16 across the 18 of their papers we have counts for
6 papers · 1 filter
Value Drifts: Tracing Value Alignment During LLM Post-Training
Mehar Bhatia, Shravan Nayak, Gaurav Kamath +4
As LLMs occupy an increasingly important role in society, they are more and more confronted with questions that require them not only to draw on their general knowledge but also to…
SPIKE-RL: Video-LLMs meet Bayesian Surprise
Sahithya Ravi, Aditya Chinchure, Raymond T. Ng +2
Real-world videos often show routine activities punctuated by memorable, surprising events. However, most Video-LLMs process videos by sampling frames uniformly, likely missing cri…
Infusing Theory of Mind into Socially Intelligent LLM Agents
EunJeong Hwang, Yuwei Yin, Giuseppe Carenini +2
Theory of Mind (ToM)-an understanding of the mental states of others-is a key aspect of human social intelligence, yet, chatbots and LLM-based social agents do not typically integr…
Response Wide Shut? Surprising Observations in Basic Vision Language Model Capabilities
Shivam Chandhok, Wan-Cyuan Fan, Vered Shwartz +2
Vision-language Models (VLMs) have emerged as general-purpose tools for addressing a variety of complex computer vision problems. Such models have been shown to be highly capable,…
BottleHumor: Self-Informed Humor Explanation using the Information Bottleneck Principle
EunJeong Hwang, Peter West, Vered Shwartz
Humor is prevalent in online communications and it often relies on more than one modality (e.g., cartoons and memes). Interpreting humor in multimodal settings requires drawing on…
Bridging Information Gaps with Comprehensive Answers: Improving the Diversity and Informativeness of Follow-Up Questions
Zhe Liu, Taekyu Kang, Haoyu Wang +2
Generating diverse follow-up questions that uncover missing information remains challenging for conversational agents, particularly when they run on small, locally hosted models. T…