2 citations · 2 across the 2 of their papers we have counts for
3 papers
cs.CL2024
Learning to Retrieve Iteratively for In-Context Learning
Yunmo Chen, Tongfei Chen, Harsh Jhamtani +4
We introduce iterative retrieval, a novel framework that empowers retrievers to make iterative decisions through policy optimization. Finding an optimal portfolio of retrieved item…
cs.CL2023★ 2 cited
ToolTalk: Evaluating Tool-Usage in a Conversational Setting
Nicholas Farn, Richard Shin
Large language models (LLMs) have displayed massive improvements in reasoning and decision-making skills and can hold natural conversations with users. Many recent works seek to au…
cs.LG2023
Privacy-Preserving In-Context Learning with Differentially Private Few-Shot Generation
Xinyu Tang, Richard Shin, Huseyin A. Inan +6
We study the problem of in-context learning (ICL) with large language models (LLMs) on private datasets. This scenario poses privacy risks, as LLMs may leak or regurgitate the priv…