Kavli Affiliate: Jia Liu
| First 5 Authors: Yue Chen, Chen Huang, Yang Deng, Wenqiang Lei, Dingnan Jin
| Summary:
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 post-hoc 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.
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