most citedAutomatically Correcting Large Language Models: Surveying the landscape of diverse self-correction strategies

24 citations · 29 across the 7 of their papers we have counts for

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cs.CL2025

Culture is Everywhere: A Call for Intentionally Cultural Evaluation

Juhyun Oh, Inha Cha, Michael Saxon +3

The prevailing ``trivia-centered paradigm'' for evaluating the cultural alignment of large language models (LLMs) is increasingly inadequate as these models become more advanced an…

cs.CL2024

VSP: Assessing the dual challenges of perception and reasoning in spatial planning tasks for VLMs

Qiucheng Wu, Handong Zhao, Michael Saxon +4

Vision language models (VLMs) are an exciting emerging class of language models (LMs) that have merged classic LM capabilities with those of image processing systems. However, the…

cs.CL2024

Lost in Translation? Translation Errors and Challenges for Fair Assessment of Text-to-Image Models on Multilingual Concepts

Michael Saxon, Yiran Luo, Sharon Levy +3

Benchmarks of the multilingual capabilities of text-to-image (T2I) models compare generated images prompted in a test language to an expected image distribution over a concept set.…

cs.CL202324 cited

Automatically Correcting Large Language Models: Surveying the landscape of diverse self-correction strategies

Liangming Pan, Michael Saxon, Wenda Xu +3

Large language models (LLMs) have demonstrated remarkable performance across a wide array of NLP tasks. However, their efficacy is undermined by undesired and inconsistent behavior…

cs.CL20231 cited

Multilingual Conceptual Coverage in Text-to-Image Models

Michael Saxon, William Yang Wang

We propose "Conceptual Coverage Across Languages" (CoCo-CroLa), a technique for benchmarking the degree to which any generative text-to-image system provides multilingual parity to…