Maintaining Common Ground in Dynamic Environments
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Abstract (arXiv)
Common grounding is the process of creating and maintaining mutual understandings, which is a critical aspect of sophisticated human communication. While various task settings have been proposed in existing literature, they mostly focus on creating common ground under static context and ignore the aspect of maintaining them overtime under dynamic context. In this work, we propose a novel task setting to study the ability of both creating and maintaining common ground in dynamic environments. Based on our minimal task formulation, we collected a large-scale dataset of 5,617 dialogues to enable fine-grained evaluation and analysis of various dialogue systems. Through our dataset analyses, we highlight novel challenges introduced in our setting, such as the usage of complex spatio-temporal expressions to create and maintain common ground. Finally, we conduct extensive experiments to assess the capabilities of our baseline dialogue system and discuss future prospects of our research.
Key facts. Cited by 9 papers in the map (data through 14 Oct 2026). Builds on 40 of its 64 references that are in the map. First posted on arXiv 29 May 2021.
Builds on
40 of 64 references.
References · 40
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28 more
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Built on by
Citing papers per year: 2023: 2; 2024: 3; 2025: 2; 2026: 2.
9 papers.
- 2024Conversational Grounding: Annotation and Analysis of Grounding Acts and Grounding Unitscited by 9
- 2023Asking More Informative Questions for Grounded Retrievalcited by 6
- 2024Towards Harnessing Large Language Models for Comprehension of Conversational Groundingcited by 3
- 2025PoliCon: Evaluating LLMs on Achieving Diverse Political Consensus Objectivescited by 2
- 2023MindDial: Belief Dynamics Tracking with Theory-of-Mind Modeling for Situated Neural Dialogue Generationcited by 1
- 2024Bridging Information Gaps in Dialogues With Grounded Exchanges Using Knowledge Graphscited by 0
- 2025Ad hoc conventions generalize to new referentscited by 0
- 2026Toward Measuring Structural Drift in LLM Communication Loopscited by 0
- 2026Using Machine Mental Imagery for Representing Common Ground in Situated Dialoguecited by 0
- 2026Using Machine Mental Imagery for Representing Common Ground in Situated Dialogue22 Apr 2026
- 2026Toward Measuring Structural Drift in LLM Communication Loops18 Mar 2026
- 2025Ad hoc conventions generalize to new referents6 Sep 2025
- 2025PoliCon: Evaluating LLMs on Achieving Diverse Political Consensus Objectives26 May 2025
- 2024Bridging Information Gaps in Dialogues With Grounded Exchanges Using Knowledge Graphs2 Aug 2024
- 2024Towards Harnessing Large Language Models for Comprehension of Conversational Grounding3 Jun 2024
- 2024Conversational Grounding: Annotation and Analysis of Grounding Acts and Grounding Units25 Mar 2024
- 2023Asking More Informative Questions for Grounded Retrieval14 Nov 2023
- 2023MindDial: Belief Dynamics Tracking with Theory-of-Mind Modeling for Situated Neural Dialogue Generation27 Jun 2023