The Open-domain Paradox for Chatbots: Common Ground as the Basis for Human-like Dialogue
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Abstract (arXiv)
There is a surge in interest in the development of open-domain chatbots, driven by the recent advancements of large language models. The "openness" of the dialogue is expected to be maximized by providing minimal information to the users about the common ground they can expect, including the presumed joint activity. However, evidence suggests that the effect is the opposite. Asking users to "just chat about anything" results in a very narrow form of dialogue, which we refer to as the "open-domain paradox". In this position paper, we explain this paradox through the theory of common ground as the basis for human-like communication. Furthermore, we question the assumptions behind open-domain chatbots and identify paths forward for enabling common ground in human-computer dialogue.
Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 21 of its 46 references that are in the map. First posted on arXiv 21 Mar 2023.
Builds on
21 of 46 references.
References · 21
- 2020Language Models are Few-Shot Learnerscited by 32,419
- 2022Training language models to follow instructions with human feedbackcited by 15,971
- 2022LaMDA: Language Models for Dialog Applicationscited by 1,220
- 2018Personalizing Dialogue Agents: I have a dog, do you have pets too?cited by 828
- 2020Recipes for building an open-domain chatbotcited by 593
- 2020Towards a Human-like Open-Domain Chatbotcited by 470
- 2021Beyond Goldfish Memory: Long-Term Open-Domain Conversationcited by 267
- 2015A Neural Conversational Modelcited by 196
- 2019The Second Conversational Intelligence Challenge (ConvAI2)cited by 179
- 2017A Knowledge-Grounded Neural Conversation Modelcited by 136
- 2019Survey on Evaluation Methods for Dialogue Systemscited by 126
- 2019Learning to Speak and Act in a Fantasy Text Adventure Gamecited by 122
9 more
- 2018From Eliza to XiaoIce: Challenges and Opportunities with Social Chatbotscited by 85
- 2018Conversational AI: The Science Behind the Alexa Prizecited by 63
- 2015Computational Sociolinguistics: A Surveycited by 35
- 2020How to Motivate Your Dragon: Teaching Goal-Driven Agents to Speak and Act in Fantasy Worldscited by 35
- 2020Open-Domain Conversational Agents: Current Progress, Open Problems, and Future Directionscited by 34
- 2021Knowledge-Grounded Dialogue Generation with a Unified Knowledge Representationcited by 32
- 2022Report from the NSF Future Directions Workshop on Automatic Evaluation of Dialog: Research Directions and Challengescited by 13
- 2022How "open" are the conversations with open-domain chatbots? A proposal for Speech Event based evaluationcited by 3
- 2022How Adults Understand What Young Children Saycited by 2
Built on by
Citing papers per year: 2023: 1; 2024: 1; 2025: 1; 2026: 1.
4 papers.
- 2023SOTOPIA: Interactive Evaluation for Social Intelligence in Language Agentscited by 367
- 2024Conversational Grounding: Annotation and Analysis of Grounding Acts and Grounding Unitscited by 9
- 2025Correct-Detect: Balancing Performance and Ambiguity Through the Lens of Coreference Resolution in LLMscited by 1
- 2026Language Has Two Parameters: Narrative-Induced Semantic Plasticity and Phase-Sensitive Interpretationcited by 0
- 2026Language Has Two Parameters: Narrative-Induced Semantic Plasticity and Phase-Sensitive Interpretation18 Aug 2026
- 2025Correct-Detect: Balancing Performance and Ambiguity Through the Lens of Coreference Resolution in LLMs17 Sep 2025
- 2024Conversational Grounding: Annotation and Analysis of Grounding Acts and Grounding Units25 Mar 2024
- 2023SOTOPIA: Interactive Evaluation for Social Intelligence in Language Agents18 Oct 2023