2 citations · 2 across the 3 of their papers we have counts for
3 papers
cs.CV2024
EvalGIM: A Library for Evaluating Generative Image Models
Melissa Hall, Oscar Mañas, Reyhane Askari-Hemmat +14
As the use of text-to-image generative models increases, so does the adoption of automatic benchmarking methods used in their evaluation. However, while metrics and datasets abound…
cs.LG2024★ 2 cited
Mission Impossible: A Statistical Perspective on Jailbreaking LLMs
Jingtong Su, Julia Kempe, Karen Ullrich
Large language models (LLMs) are trained on a deluge of text data with limited quality control. As a result, LLMs can exhibit unintended or even harmful behaviours, such as leaking…
cs.CL2024
Understanding and Mitigating Tokenization Bias in Language Models
Buu Phan, Marton Havasi, Matthew Muckley +1
State-of-the-art language models are autoregressive and operate on subword units known as tokens. Specifically, one must encode the conditioning string into a list of tokens before…