2 citations · 2 across the 2 of their papers we have counts for
7 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…
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…
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…
Better Slow than Sorry: Introducing Positive Friction for Reliable Dialogue Systems
Mert Ä°nan, 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…
Selective "Selective Prediction": Reducing Unnecessary Abstention in Vision-Language Reasoning
Tejas Srinivasan, Jack Hessel, Tanmay Gupta +4
Selective prediction minimizes incorrect predictions from vision-language models (VLMs) by allowing them to abstain from answering when uncertain. However, when deploying a vision-…