Conversational Grounding in Large Language Models: Evaluation Methods, Challenges and Future Directions

Michelle Elizabeth, Gwénolé Lecorvé, Lina M. Rojas Barahona, Magalie Ochs


Abstract
Conversational grounding is the collaborative process through which speakers establish and maintain mutual understanding. It is essential for the success of a dialogue. While it is inherent in human conversations, it remains a challenge for instruction-following Large Language Models (LLM). This paper surveys how conversational grounding is evaluated in task-oriented dialogue in the current era of LLMs. First, we focus on how conversational grounding is modelled explicitly — using dialogue acts and by modelling the participant mental state. Then, we review collaborative tasks that enable the evaluation of conversational grounding implicitly at the global level based on outcomes. Finally, we highlight the limitations of evaluation, notably the current methodology and metrics used, and outline research directions in conversational grounding and its evaluation.
Anthology ID:
2026.sigdial-1.11
Volume:
Proceedings of the 27th Annual Meeting of the Special Interest Group on Discourse and Dialogue
Month:
August
Year:
2026
Address:
Atlanta, Georgia, USA
Editors:
Jinho D. Choi, Yun-Nung Chen, Kotaro Funakoshi, Ali Emami
Venue:
SIGDIAL
SIG:
SIGDIAL
Publisher:
Association for Computational Linguistics
Note:
Pages:
151–163
Language:
URL:
https://aclanthology.org/2026.sigdial-1.11/
DOI:
Bibkey:
Cite (ACL):
Michelle Elizabeth, Gwénolé Lecorvé, Lina M. Rojas Barahona, and Magalie Ochs. 2026. Conversational Grounding in Large Language Models: Evaluation Methods, Challenges and Future Directions. In Proceedings of the 27th Annual Meeting of the Special Interest Group on Discourse and Dialogue, pages 151–163, Atlanta, Georgia, USA. Association for Computational Linguistics.
Cite (Informal):
Conversational Grounding in Large Language Models: Evaluation Methods, Challenges and Future Directions (Elizabeth et al., SIGDIAL 2026)
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PDF:
https://aclanthology.org/2026.sigdial-1.11.pdf