3 citations · 4 across the 9 of their papers we have counts for
16 papers · 1 filter
Believing without Seeing: Quality Scores for Contextualizing Vision-Language Model Explanations
Keyu He, Tejas Srinivasan, Brihi Joshi +3
When people query Vision-Language Models (VLMs) but cannot see the accompanying visual context (e.g. for blind and low-vision users), augmenting VLM predictions with natural langua…
From Calibration to Collaboration: LLM Uncertainty Quantification Should Be More Human-Centered
Siddartha Devic, Tejas Srinivasan, Jesse Thomason +2
Large Language Models (LLMs) are increasingly assisting users in the real world, yet their reliability remains a concern. Uncertainty quantification (UQ) has been heralded as a too…
Can Vision Language Models Understand Mimed Actions?
Hyundong Cho, Spencer Lin, Tejas Srinivasan +4
Nonverbal communication (NVC) plays an integral role in human language, but studying NVC in general is challenging because of its broad scope and high variance in interpretation am…
Better Slow than Sorry: Introducing Positive Friction for Reliable Dialogue Systems
Mert İnan, Anthony Sicilia, Suvodip Dey +6
While theories of discourse and cognitive science have long recognized the value of unhurried pacing, recent dialogue research tends to minimize friction in conversational systems.…
Compare without Despair: Reliable Preference Evaluation with Generation Separability
Sayan Ghosh, Tejas Srinivasan, Swabha Swayamdipta
Human evaluation of generated language through pairwise preference judgments is pervasive. However, under common scenarios, such as when generations from a model pair are very simi…
WinoViz: Probing Visual Properties of Objects Under Different States
Woojeong Jin, Tejas Srinivasan, Jesse Thomason +1
Humans perceive and comprehend different visual properties of an object based on specific contexts. For instance, we know that a banana turns brown ``when it becomes rotten,'' wher…