4 citations · 4 across the 4 of their papers we have counts for
4 papers
VibeCheck: Discover and Quantify Qualitative Differences in Large Language Models
Lisa Dunlap, Krishna Mandal, Trevor Darrell +2
Large language models (LLMs) often exhibit subtle yet distinctive characteristics in their outputs that users intuitively recognize, but struggle to quantify. These "vibes" -- such…
Language Models Learn to Mislead Humans via RLHF
Jiaxin Wen, Ruiqi Zhong, Akbir Khan +6
Language models (LMs) can produce errors that are hard to detect for humans, especially when the task is complex. RLHF, the most popular post-training method, may exacerbate this p…
Explaining Datasets in Words: Statistical Models with Natural Language Parameters
Ruiqi Zhong, Heng Wang, Dan Klein +1
To make sense of massive data, we often fit simplified models and then interpret the parameters; for example, we cluster the text embeddings and then interpret the mean parameters…
Describing Differences in Image Sets with Natural Language
Lisa Dunlap, Yuhui Zhang, Xiaohan Wang +5
How do two sets of images differ? Discerning set-level differences is crucial for understanding model behaviors and analyzing datasets, yet manually sifting through thousands of im…