10 citations · 13 across the 3 of their papers we have counts for
3 papers
cs.LG2024★ 2 cited
Grounding by Trying: LLMs with Reinforcement Learning-Enhanced Retrieval
Sheryl Hsu, Omar Khattab, Chelsea Finn +1
The hallucinations of large language models (LLMs) are increasingly mitigated by allowing LLMs to search for information and to ground their answers in real sources. Unfortunately,…
cs.CR2024★ 10 cited
What is in the Chrome Web Store? Investigating Security-Noteworthy Browser Extensions
Sheryl Hsu, Manda Tran, Aurore Fass
This paper is the first attempt at providing a holistic view of the Chrome Web Store (CWS). We leverage historical data provided by ChromeStats to study global trends in the CWS an…
cs.LG2024★ 1 cited
RLVF: Learning from Verbal Feedback without Overgeneralization
Moritz Stephan, Alexander Khazatsky, Eric Mitchell +4
The diversity of contexts in which large language models (LLMs) are deployed requires the ability to modify or customize default model behaviors to incorporate nuanced requirements…