q2d: Turning Questions into Dialogs to Teach Models How to Search

Yonatan Bitton, Shlomi Cohen-Ganor, Ido Hakimi, Yoad Lewenberg, Roee Aharoni, Enav Weinreb

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

One of the exciting capabilities of recent language models for dialog is their ability to independently search for relevant information to ground a given dialog response. However, obtaining training data to teach models how to issue search queries is time and resource consuming. In this work, we propose q2d: an automatic data generation pipeline that generates information-seeking dialogs from questions. We prompt a large language model (PaLM) to create conversational versions of question answering datasets, and use it to improve query generation models that communicate with external search APIs to ground dialog responses. Unlike previous approaches which relied on human written dialogs with search queries, our method allows to automatically generate query-based grounded dialogs with better control and scale. Our experiments demonstrate that: (1) For query generation on the QReCC dataset, models trained on our synthetically-generated data achieve 90%-97% of the performance of models trained on the human-generated data; (2) We can successfully generate data for training dialog models in new domains without any existing dialog data as demonstrated on the multi-hop MuSiQue and Bamboogle QA datasets. (3) We perform a thorough analysis of the generated dialogs showing that humans find them of high quality and struggle to distinguish them from human-written dialogs.

Original languageEnglish
Title of host publicationEMNLP 2023 - 2023 Conference on Empirical Methods in Natural Language Processing, Proceedings
EditorsHouda Bouamor, Juan Pino, Kalika Bali
PublisherAssociation for Computational Linguistics (ACL)
Pages13661-13676
Number of pages16
ISBN (Electronic)9798891760608
StatePublished - 2023
Externally publishedYes
Event2023 Conference on Empirical Methods in Natural Language Processing, EMNLP 2023 - Hybrid, Singapore, Singapore
Duration: 6 Dec 202310 Dec 2023

Publication series

NameEMNLP 2023 - 2023 Conference on Empirical Methods in Natural Language Processing, Proceedings

Conference

Conference2023 Conference on Empirical Methods in Natural Language Processing, EMNLP 2023
Country/TerritorySingapore
CityHybrid, Singapore
Period6/12/2310/12/23

Bibliographical note

Publisher Copyright:
©2023 Association for Computational Linguistics.

Fingerprint

Dive into the research topics of 'q2d: Turning Questions into Dialogs to Teach Models How to Search'. Together they form a unique fingerprint.

Cite this