Frame of Reference: Addressing the Challenges of Common Ground Representation in Situational Dialogs

2026-01-14

AI Summary

This work addresses the challenge that existing dialogue systems struggle to effectively establish and leverage common ground, often resulting in incoherent referential expressions in context-dependent conversations. To overcome this limitation, the authors propose a novel mechanism that integrates large language models with structured relational representations to explicitly model, store, and retrieve entities, events, and their interrelationships throughout a dialogue. This approach enables reasoning grounded in shared context and supports relational reference resolution. The designed memory architecture and dialogue policy not only facilitate clarification and confirmation but also dynamically maintain common ground over the course of interaction. Experimental results demonstrate that the proposed method significantly improves both the accuracy of common ground establishment and the consistency of subsequent referential utterances, outperforming current state-of-the-art baselines.

Technology Category

• Natural Language Processing: Conversational AI/Dialog Systems
• Knowledge Representation and Reasoning: Common-Sense Reasoning
• Cognitive Modeling & Cognitive Systems: Conceptual Inference and Reasoning

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Search and Retrieval-Augmented AI: Personalized, context-aware and across-device search
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

Common ground plays a critical role in situated spoken dialogues, where interlocutors must establish and maintain shared references to entities, events, and relations to sustain coherent interaction. For dialog systems, the ability to correctly ground conversational content in order to refer back to it later is particularly important. Prior studies have demonstrated that LLMs are capable of performing grounding acts such as requesting clarification or producing acknowledgments, yet relatively little work has investigated how common ground can be explicitly represented and stored for later use. Without such mechanisms, it remains unclear whether acknowledgment or clarification behaviors truly reflect a grounded understanding. In this work, we evaluate a model's ability to establish and exploit common ground through relational references to entities within the shared context in a situational dialogue. We test multiple methods for representing common ground in situated dialogues and further propose approaches to improve both the establishment of common ground and its subsequent use in the conversation.

Problem

• common ground
• situated dialogue
• relational reference
• dialog systems
• grounding

Innovation

• common ground
• situated dialogue
• relational reference
• dialogue grounding
• LLM evaluation