activity
20232026
most citedHow FaR Are Large Language Models From Agents with Theory-of-Mind?

6 citations · 11 across the 7 of their papers we have counts for

collaborators

7 papers

cs.AI2026

Rich Insights from Cheap Signals: Efficient Evaluations via Tensor Factorization

Felipe Maia Polo, Aida Nematzadeh, Virginia Aglietti +2

Moving beyond evaluations that collapse performance across heterogeneous prompts toward fine-grained evaluation at the prompt level, or within relatively homogeneous subsets, is ne…

cs.CV2025

Benchmarking Diversity in Image Generation via Attribute-Conditional Human Evaluation

Isabela Albuquerque, Ira Ktena, Olivia Wiles +4

Despite advances in generation quality, current text-to-image (T2I) models often lack diversity, generating homogeneous outputs. This work introduces a framework to address the nee…

cs.LG2025

Dynamic Classifier-Free Diffusion Guidance via Online Feedback

Pinelopi Papalampidi, Olivia Wiles, Ira Ktena +5

Classifier-free guidance (CFG) is a cornerstone of text-to-image diffusion models, yet its effectiveness is limited by the use of static guidance scales. This "one-size-fits-all" a…

cs.CV2024★ 4 cited

Imagen 3

Imagen-Team-Google, :, Jason Baldridge +257

We introduce Imagen 3, a latent diffusion model that generates high quality images from text prompts. We describe our quality and responsibility evaluations. Imagen 3 is preferred…

cs.LG2024★ 1 cited

Evaluating Numerical Reasoning in Text-to-Image Models

Ivana Kajić, Olivia Wiles, Isabela Albuquerque +4

Text-to-image generative models are capable of producing high-quality images that often faithfully depict concepts described using natural language. In this work, we comprehensivel…

cs.CV2024

Revisiting Text-to-Image Evaluation with Gecko: On Metrics, Prompts, and Human Ratings

Olivia Wiles, Chuhan Zhang, Isabela Albuquerque +11

While text-to-image (T2I) generative models have become ubiquitous, they do not necessarily generate images that align with a given prompt. While previous work has evaluated T2I al…

cs.CL2023★ 6 cited

How FaR Are Large Language Models From Agents with Theory-of-Mind?

Pei Zhou, Aman Madaan, Srividya Pranavi Potharaju +9

"Thinking is for Doing." Humans can infer other people's mental states from observations--an ability called Theory-of-Mind (ToM)--and subsequently act pragmatically on those infere…