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Building Common Ground in Dialogue: A Survey

Summary

Common ground is the knowledge, beliefs and goals that interlocutors share. It is necessary for mutual understanding, but the term is interpreted in different ways in NLP. It is mostly not taken into account when chat-oriented language models are evaluated. The authors wrote a survey that systematizes the concept. They distinguish two senses of grounding. Communicative grounding is the gradual achievement of mutual understanding in dialogue. Symbolic grounding is the linking of words to the world, images or documents. Approaches are classified along three dimensions: modality (textual, visual, multimodal), scope of knowledge (general, domain-specific, contextual) and type (static or dynamic). The source base consists of 448 papers from the ACL Anthology published since 2015. They were selected by keywords and supplemented through citation chains. The authors also compiled a list of available datasets and released their annotations. The survey describes the modeling of grounding in knowledge, images and persona. It examines the problems that arise from a lack of common ground in human–LM interaction. It outlines gaps: there is a shortage of multimodal work, dynamic scenarios and evaluation studies. The paper can serve as a guide for developers of dialogue systems capable of grounding.

Key points

  • The survey covers 448 papers from the ACL Anthology published since 2015. In their abstracts, "common ground" or "grounding" co-occurs with "dialogue". Irrelevant papers were filtered out, and a few were added through citations.
  • Three dimensions of common ground are proposed: modality, scope and type (static vs dynamic).
  • The authors clustered the abstracts with k-means. Almost 16% of the papers are devoted to grounded generation and selection, and 15.2% to cognitive and human-centered aspects. Knowledge grounding and fine-tuning LLM account for 13% each, multimodal work for 7.4%, and spatial grounding for 5.37%. Only 2% of the papers are devoted to evaluation and benchmarks.
  • The surveyed works show that LLM-based evaluation correlates poorly with human judgments and is not robust to perturbations. Knowledge-grounded dialogues should be checked more thoroughly for hallucinations, and human evaluation should be involved.
  • Future directions:
    • integrating common ground into pre-training and fine-tuning, for example through auxiliary loss functions and contrastive learning;
    • more research on dynamic grounding;
    • collecting more diverse multimodal datasets;
    • developing collaboration with robotics, cognitive science and linguistics.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

Fields: Linguistics & languages

Cite

The paper itself, not this page. BibTeX from the library's record of the work.

@inproceedings{anikina2025building,
  title = {{Building Common Ground in Dialogue: A Survey}},
  author = {Anikina, Tatiana and Leippert, Alina and Ostermann, Simon},
  booktitle = {Proceedings of the 2nd LUHME Workshop},
  year = {2025},
  url = {https://aclanthology.org/2025.luhme-1.2/}
}

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