1 citations · 1 across the 3 of their papers we have counts for
3 papers
cs.AI2023★ 1 cited
SCREWS: A Modular Framework for Reasoning with Revisions
Kumar Shridhar, Harsh Jhamtani, Hao Fang +3
Large language models (LLMs) can improve their accuracy on various tasks through iteratively refining and revising their output based on feedback. We observe that these revisions c…
cs.CL2023
Few-Shot Adaptation for Parsing Contextual Utterances with LLMs
Kevin Lin, Patrick Xia, Hao Fang
We evaluate the ability of semantic parsers based on large language models (LLMs) to handle contextual utterances. In real-world settings, there typically exists only a limited num…
cs.CL2021
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…