24 citations · 28 across the 4 of their papers we have counts for
4 papers
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…
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…
Data Augmentation for Diverse Voice Conversion in Noisy Environments
Avani Tanna, Michael Saxon, Amr El Abbadi +1
Voice conversion (VC) models have demonstrated impressive few-shot conversion quality on the clean, native speech populations they're trained on. However, when source or target spe…
Users are the North Star for AI Transparency
Alex Mei, Michael Saxon, Shiyu Chang +2
Despite widespread calls for transparent artificial intelligence systems, the term is too overburdened with disparate meanings to express precise policy aims or to orient concrete…