81 citations · 275 across the 51 of their papers we have counts for
3 papers · 1 filter
NumBench: Diagnosing Counting Failures in Text-to-Image Models
Sandeep Wadhwa, Mayank Vatsa, Richa Singh +2
Text-to-image (T2I) models often generate the wrong number of objects, yet existing benchmarks are too small or weakly controlled to explain why. We introduce \textbf{NumBench}, a…
Latent Performance Profiling of Large Language Models
Tanmoy Chakraborty, Ayan Sengupta, Suparna Bhattacharya +7
Large language models (LLMs) frequently achieve impressive scores on standardized benchmarks, yet accuracy alone offers a limited view of their capabilities. Evaluating open-source…
Unbiased Model Prediction Without Using Protected Attribute Information
Puspita Majumdar, Surbhi Mittal, Saheb Chhabra +2
The problem of bias persists in the deep learning community as models continue to provide disparate performance across different demographic subgroups. Therefore, several algorithm…