8 citations · 17 across the 5 of their papers we have counts for
5 papers
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…
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…
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…
BenchCLAMP: A Benchmark for Evaluating Language Models on Syntactic and Semantic Parsing
Subhro Roy, Sam Thomson, Tongfei Chen +4
Recent work has shown that generation from a prompted or fine-tuned language model can perform well at semantic parsing when the output is constrained to be a valid semantic repres…
Pruning Pretrained Encoders with a Multitask Objective
Patrick Xia, Richard Shin
The sizes of pretrained language models make them challenging and expensive to use when there are multiple desired downstream tasks. In this work, we adopt recent strategies for mo…