Incorporating Query Recommendation for Improving In-Car Conversational Search

Retrieval-augmented generation has become an effective mechanism for conversational systems in domain-specific settings. Retrieval of a wrong document due to the lack of context from the user utterance may lead to wrong answer generation. Such an issue may reduce the user engagement and thereby the system reliability. In this paper, we propose a context-guided follow-up question recommendation to internally improve the document retrieval in an iterative approach for developing an in-car conversational system. Specifically, a user utterance is first reformulated, given the context of the conversation to facilitate improved understanding to the retriever. In the cases, where the documents retrieved by the retriever are not relevant enough for answering the user utterance, we employ a large language model (LLM) to generate question recommendation which is then utilized to perform a refined retrieval. An empirical evaluation confirms the effectiveness of our proposed approaches in in-car conversations, achieving 48% and 22% improvement in the retrieval and system generated responses, respectively, against baseline approaches.