@inproceedings{lin2023few-shot,
author = {Lin, Kevin and Xia, Patrick and Fang, Hao},
title = {Few-Shot Adaptation for Parsing Contextual Utterances with LLMs},
booktitle = {Findings of IJCNLP-AACL 2023},
year = {2023},
month = {November},
abstract = {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 number of annotated contextual utterances due to annotation cost, resulting in an imbalance compared to non-contextual utterances. Therefore, parsers must adapt to contextual utterances with a few training examples. We examine four major paradigms for doing so in conversational semantic parsing i.e., Parse-with-Utterance-History, Parse-with-Reference-Program, Parse-then-Resolve, and Rewrite-then-Parse. To facilitate such cross-paradigm comparisons, we construct SMCalFlow-EventQueries, a subset of contextual examples from SMCalFlow with additional annotations. Experiments with in-context learning and fine-tuning suggest that Rewrite-then-Parse is the most promising paradigm when holistically considering parsing accuracy, annotation cost, and error types.},
url = {http://approjects.co.za/?big=en-us/research/publication/few-shot-adaptation-for-parsing-contextual-utterances-with-llms/},
}