Conversational search provides a more convenient interface for users to
search by allowing multi-turn interaction with the search engine. However, the
effectiveness of the conversational dense retrieval methods is limited by the
scarcity of training data required for their fine-tuning. Thus, generating more
training conversational sessions with relevant labels could potentially improve
search performance. Based on the promising capabilities of large language
models (LLMs) on text generation, we propose ConvSDG, a simple yet effective
framework to explore the feasibility of boosting conversational search by using
LLM for session data generation. Within this framework, we design
dialogue/session-level and query-level data generation with unsupervised and
semi-supervised learning, according to the availability of relevance judgments.
The generated data are used to fine-tune the conversational dense retriever.
Extensive experiments on four widely used datasets demonstrate the
effectiveness and broad applicability of our ConvSDG framework compared with
several strong baselines.

基于大型语言模型的 ConvSDG 框架通过生成对话数据来提高对话式搜索的性能，并在四个广泛使用的数据集上展现了其有效性和广泛适用性。

ConvSDG：面向对话式搜索的会话数据生成

ConvSDG: Session Data Generation for Conversational Search

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。