Grounded and Transparent Response Generation for Conversational Information-Seeking Systems

TL;DR

This paper explores methods for generating grounded, transparent, and trustworthy responses in conversational information-seeking systems by addressing response synthesis, confidence estimation, and system limitations communication.

Contribution

It introduces novel techniques for grounding responses in retrieved passages and transparently communicating system limitations in CIS dialogues.

Findings

Enhanced response coherence and trustworthiness

Automatic detection of incomplete or incorrect responses

Improved system transparency and user trust

Abstract

While previous conversational information-seeking (CIS) research has focused on passage retrieval, reranking, and query rewriting, the challenge of synthesizing retrieved information into coherent responses remains. The proposed research delves into the intricacies of response generation in CIS systems. Open-ended information-seeking dialogues introduce multiple challenges that may lead to potential pitfalls in system responses. The study focuses on generating responses grounded in the retrieved passages and being transparent about the system's limitations. Specific research questions revolve around obtaining confidence-enriched information nuggets, automatic detection of incomplete or incorrect responses, generating responses communicating the system's limitations, and evaluating enhanced responses. By addressing these research tasks the study aspires to contribute to the advancement of conversational response generation, fostering more trustworthy interactions in CIS dialogues, and paving the way for grounded and transparent systems to meet users' needs in an information-driven world.