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Control in Hybrid Chatbots

arXiv 2311.11701 ↗First posted 20 Nov 2023cs.IR cs.AI cs.CLcited by 0

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Abstract (arXiv)

Customer data typically is held in database systems, which can be seen as rule-based knowledge base, whereas businesses increasingly want to benefit from the capabilities of large, pre-trained language models. In this technical report, we describe a case study of how a commercial rule engine and an integrated neural chatbot may be integrated, and what level of control that particular integration mode leads to. We also discuss alternative ways (including past ways realized in other systems) how researchers strive to maintain control and avoid what has recently been called model "hallucination".

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 8 of its 25 references that are in the map. First posted on arXiv 20 Nov 2023.

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8 of 25 references.

References · 8

  1. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,297
  2. 2019DialoGPT: Large-Scale Generative Pre-training for Conversational Response Generationcited by 842
  3. 2020Recipes for building an open-domain chatbotcited by 593
  4. 2020Towards a Human-like Open-Domain Chatbotcited by 470
  5. 2023A Survey of Hallucination in Large Foundation Modelscited by 368
  6. 2020Generation-Augmented Retrieval for Open-domain Question Answeringcited by 186
  7. 2018The Design and Implementation of XiaoIce, an Empathetic Social Chatbotcited by 172
  8. 2018Retrieve and Refine: Improved Sequence Generation Models For Dialoguecited by 82

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