5 papers · 1 filter
Thesis Proposal: Toward a Human-Centered and Perspective-Aware Framework for Reproducible ML Evaluation and AI Alignment
Deepak Pandita, Christopher M. Homan
Humans play a vital role at every stage of AI development, from data collection and curation to model development and evaluation. However, humans often disagree with each other and…
RamseyGadgets: A Graph Construction Dataset for LLMs
Zohair Raza Hassan, Deepak Pandita
Constructing special graphs is an important task within graph theory and computer science. Many popular graph constructions are the result of a comprehensive exploration of relevan…
Pluralis v0.1: Towards a Multicultural, Multimodal, Multilingual Benchmark for AI Risk and Reliability
Alicia Parrish, Rajat Shinde, Sanket Badhe +57
Current AI safety evaluation and benchmarking frameworks predominantly rely on Western-centric culture-agnostic defaults that mask critical regional laws, socio-linguistic nuances,…
Improving Reproducibility in Evaluation through Multi-Level Annotator Modeling
Deepak Pandita, Flip Korn, Chris Welty +1
As generative AI models such as large language models (LLMs) become more pervasive, ensuring the safety, robustness, and overall trustworthiness of these systems is paramount. Howe…
Learning Who Disagrees: Demographic Importance Weighting for Modeling Annotator Distributions with DiADEM
Samay U. Shetty, Tharindu Cyril Weerasooriya, Deepak Pandita +1
When humans label subjective content, they disagree, and that disagreement is not noise. It reflects genuine differences in perspective shaped by annotators' social identities and…