Publication Type
Conference Proceeding Article
Version
publishedVersion
Publication Date
8-2024
Abstract
Equipping a conversational search engine with strategies regarding when to ask clarification questions is becoming increasingly important across various domains. Attributing to the context understanding capability of LLMs and their access to domain-specific sources of knowledge, LLM-based clarification strategies feature rapid transfer to various domains in a posthoc manner. However, they still struggle to deliver promising performance on unseen domains, struggling to achieve effective domain transferability. We take the first step to investigate this issue and existing methods tend to produce one-size-fits-all strategies across diverse domains, limiting their search effectiveness. In response, we introduce a novel method, called STYLE, to achieve effective domain transferability. Our experimental results indicate that STYLE bears strong domain transferability, resulting in an average search performance improvement of ∼10% on four unseen domains.
Discipline
Databases and Information Systems | Programming Languages and Compilers
Research Areas
Data Science and Engineering
Publication
Findings of the Association for Computational Linguistics ACL 2024, Bangkok, Thailand, August 11-16
First Page
10633
Last Page
10649
Publisher
ACL
City or Country
USA
Citation
CHEN, Yue; HUANG, Chen; DENG, Yang; LEI, Wenqiang; JIN, Dingnan; LIU, Jia; and CHUA, Tat-Seng.
STYLE: Improving domain transferability of asking clarification questions in large language model powered conversational agents. (2024). Findings of the Association for Computational Linguistics ACL 2024, Bangkok, Thailand, August 11-16. 10633-10649.
Available at: https://ink.library.smu.edu.sg/sis_research/9234
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.