Explainability for Transparent Conversational Information-Seeking
The increasing reliance on digital information necessitates advancements in conversational search systems, particularly in terms of information transparency. While prior research in conversational information-seeking has concentrated on improving retrieval techniques, the challenge remains in generating responses useful from a user perspective. This study explores different methods of explaining the responses, hypothesizing that transparency about the source of the information, system confidence, and limitations can enhance users' ability to objectively assess the response. By exploring transparency across explanation type, quality, and presentation mode, this research aims to bridge the gap between system-generated responses and responses verifiable by the user. We design a user study to answer questions concerning the impact of (1) the quality of explanations enhancing the response on its usefulness and (2) ways of presenting explanations to users. The analysis of the collected data reveals lower user ratings for noisy explanations, although these scores seem insensitive to the quality of the response. Inconclusive results on the explanations presentation format suggest that it may not be a critical factor in this setting.
Code (1)
Tasks
Conversational Response GenerationConversational SearchSimilar Papers 제목 키워드 기반
Grounded and Transparent Response Generation for Conversational Information-Seeking Systems
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 …
Conversational Response GenerationPassage RetrievalRerankingResponse Generation+1A Large-Scale Analysis of Mixed Initiative in Information-Seeking Dialogues for Conversational Search
Conversational search is a relatively young area of research that aims at automating an information-seeking dialogue. In this paper we help to position it with respect to other research areas within conversational Artifi…
Conversational SearchEvaluating Search System Explainability with Psychometrics and Crowdsourcing
As information retrieval (IR) systems, such as search engines and conversational agents, become ubiquitous in various domains, the need for transparent and explainable systems grows to ensure accountability, fairness, an…
FairnessInformation RetrievalRecommendation SystemsRetrievalUser Intent Prediction in Information-seeking Conversations
Conversational assistants are being progressively adopted by the general population. However, they are not capable of handling complicated information-seeking tasks that involve multiple turns of information exchange. Du…
Conversational SearchFeature EngineeringFeature ImportancePredictionConversational Information Seeking
Conversational information seeking (CIS) is concerned with a sequence of interactions between one or more users and an information system. Interactions in CIS are primarily based on natural language dialogue, while they …
Conversational Question AnsweringConversational RecommendationConversational SearchQuestion Answering