Conversational search provides a natural interface for information retrieval
(IR). Recent approaches have demonstrated promising results in applying dense
retrieval to conversational IR. However, training dense retrievers requires
large amounts of in-domain paired data. This hinders the development of
conversational dense retrievers, as abundant in-domain conversations are
expensive to collect. In this paper, we propose CONVERSER, a framework for
training conversational dense retrievers with at most 6 examples of in-domain
dialogues. Specifically, we utilize the in-context learning capability of large
language models to generate conversational queries given a passage in the
retrieval corpus. Experimental results on conversational retrieval benchmarks
OR-QuAC and TREC CAsT 19 show that the proposed CONVERSER achieves comparable
performance to fully-supervised models, demonstrating the effectiveness of our
proposed framework in few-shot conversational dense retrieval. All source code
and generated datasets are available at this https URL

使用 CONVERSER 框架，在最多 6 个领域对话示例的情况下，利用大规模语言模型的上下文学习能力为基于对话的密集重排进行训练，实验结果表明所提出的框架在少样本对话密集重排中取得了可比较的性能。