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Towards Human-centered Proactive Conversational Agents

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arxiv 2404.12670 v1 pith:5XLEAYNX submitted 2024-04-19 cs.IR cs.CLcs.HC

classification cs.IRcs.CLcs.HC
keywords proactiveconversationalhuman-centeredsystemagentspcasresearchtowards
verification ladder T0 review T1 audit T2 compute T3 formal
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Recent research on proactive conversational agents (PCAs) mainly focuses on improving the system's capabilities in anticipating and planning action sequences to accomplish tasks and achieve goals before users articulate their requests. This perspectives paper highlights the importance of moving towards building human-centered PCAs that emphasize human needs and expectations, and that considers ethical and social implications of these agents, rather than solely focusing on technological capabilities. The distinction between a proactive and a reactive system lies in the proactive system's initiative-taking nature. Without thoughtful design, proactive systems risk being perceived as intrusive by human users. We address the issue by establishing a new taxonomy concerning three key dimensions of human-centered PCAs, namely Intelligence, Adaptivity, and Civility. We discuss potential research opportunities and challenges based on this new taxonomy upon the five stages of PCA system construction. This perspectives paper lays a foundation for the emerging area of conversational information retrieval research and paves the way towards advancing human-centered proactive conversational systems.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Habit Coach: Customising RAG-based chatbots to support behavior change

    cs.HC 2024-11 conditional novelty 6.0 of 10

    Encoding step-by-step therapy procedures in the system prompt made a GPT-4 habit-change chatbot feel more effective than retrieving textbook knowledge, and five users reported large habit-strength reductions in a pilot.

  2. Knowledge-Enhanced Conversational Recommendation via Transformer-based Sequential Modelling

    cs.IR 2024-12 conditional novelty 4.0 of 10

    Sequential modeling of mentioned items and entities with a masked-item Transformer improves conversational recommendation, and a knowledge-graph-augmented variant performs best.

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