activity
20242026
most citedImproving Text-to-Image Consistency via Automatic Prompt Optimization

6 citations · 10 across the 9 of their papers we have counts for

collaborators

9 papers

cs.CY2026

Muse Spark Safety & Preparedness Report

Cristina Menghini, Peter Ney, Hamza Kwisaba +117

Muse Spark is the latest large language model developed by Meta. In this report, we first present evaluations for catastrophic risk domains under Meta's Advanced AI Scaling Framewo…

cs.LG2025

Eval Factsheets: A Structured Framework for Documenting AI Evaluations

Florian Bordes, Candace Ross, Justine T Kao +2

The rapid proliferation of benchmarks has created significant challenges in reproducibility, transparency, and informed decision-making. However, unlike datasets and models -- whic…

cs.CL2024

What makes a good metric? Evaluating automatic metrics for text-to-image consistency

Candace Ross, Melissa Hall, Adriana Romero Soriano +1

Language models are increasingly being incorporated as components in larger AI systems for various purposes, from prompt optimization to automatic evaluation. In this work, we anal…

cs.CL2024

Improving Model Evaluation using SMART Filtering of Benchmark Datasets

Vipul Gupta, Candace Ross, David Pantoja +3

One of the most challenging problems facing NLP today is evaluation. Some of the most pressing issues pertain to benchmark saturation, data contamination, and diversity in the qual…

cs.CV2024

Decomposed evaluations of geographic disparities in text-to-image models

Abhishek Sureddy, Dishant Padalia, Nandhinee Periyakaruppa +6

Recent work has identified substantial disparities in generated images of different geographic regions, including stereotypical depictions of everyday objects like houses and cars.…

cs.CL2024★ 1 cited

Changing Answer Order Can Decrease MMLU Accuracy

Vipul Gupta, David Pantoja, Candace Ross +2

As large language models (LLMs) have grown in prevalence, particular benchmarks have become essential for the evaluation of these models and for understanding model capabilities. M…

cs.CV2024★ 1 cited

Towards Geographic Inclusion in the Evaluation of Text-to-Image Models

Melissa Hall, Samuel J. Bell, Candace Ross +3

Rapid progress in text-to-image generative models coupled with their deployment for visual content creation has magnified the importance of thoroughly evaluating their performance…

cs.CL2024★ 2 cited

[Call for Papers] The 2nd BabyLM Challenge: Sample-efficient pretraining on a developmentally plausible corpus

Leshem Choshen, Ryan Cotterell, Michael Y. Hu +7

After last year's successful BabyLM Challenge, the competition will be hosted again in 2024/2025. The overarching goals of the challenge remain the same; however, some of the compe…

cs.CV2024★ 6 cited

Improving Text-to-Image Consistency via Automatic Prompt Optimization

Oscar Mañas, Pietro Astolfi, Melissa Hall +6

Impressive advances in text-to-image (T2I) generative models have yielded a plethora of high performing models which are able to generate aesthetically appealing, photorealistic im…