Building Common Ground in Dialogue: A Survey (2025)

Abstract: Common ground plays a crucial role in human communication and the grounding process helps to establish shared knowledge. However, common ground is also a heavily loaded term that may be interpreted in different ways depending on the context. The scope of common ground ranges from domain-specific and personal shared experiences to common sense knowledge. Representationally, common ground can be uni- or multi-modal, and static or dynamic. In this survey, we attempt to systematize different facets of common ground in dialogue and position it within the current landscape of NLP research that often relies on the usage of language models (LMs) and task-specific short-term interactions. We outline different dimensions of common ground and describe modeling approaches for several grounding tasks, discuss issues caused by the lack of common ground in human-LM interactions, and suggest future research directions. This survey serves as a roadmap of what to pay attention to when equipping a dialogue system with grounding capabilities and provides a summary of current research on grounding in dialogue, categorizing 448 papers and compiling a list of the available datasets.

## introduction common ground has been studied in a variety of settings by linguists, computer scientists, and philosophers alike (see e.g. foundational work by clark and brennan @xcite , stalnaker @xcite , benotti and blackburn @xcite ). common ground in dialogue can be defined as a set of shared beliefs between the interlocutors. however, as pointed out in markowska et al. @xcite , a more complete definition should also include other components such as shared desires, intentions, and goals. according to larsson @xcite , grounding has two core "meanings". the first meaning is called symbol grounding and it is concerned with the process of mapping symbols (e.g., words) to perception and the world (e.g., objects, documents, images etc.). the second meaning refers to the process of interactively adding to common ground in dialogue, i.e. communicative grounding. in natural language processing (nlp), the former meaning of symbol grounding is more commonly used, whereas in fields such as cognitive science and dialogue theory, grounding typically refers to the process of achieving mutual understanding, i.e. the latter meaning of communicative grounding. in this survey we consider both meanings and cover some relevant works on communicative grounding in section 3 and focus mostly on symbol grounding in section 4. furthermore, section 5 describes the process of building common ground (both communicative and symbolic) in the context of language models (lms). from a natural language processing perspective, conversational grounding is important for building trustworthy dialogue systems that can reliably use shared knowledge in conversation @xcite . despite the widespread usage of chat-based lms, common ground is still often overlooked and not evaluated when comparing the performance of different models. the question arises: can we trust lms and their generated outputs without building some common ground first? ideally, interactions should happen in such a way that common ground is built between users and lms for more efficient and trustworthy communication. in order to address this question, it is necessary to clearly define and separate different dimensions of common ground and also to reflect on how these can be modeled and evaluated. to that end, the contributions of this survey are as follows: ## methodology this survey focuses less on the theory of common ground (see section 3 for definitions and some examples) and more on how it has been realized in nlp models (see section 4 and 5). in order to survey current research and also cover a variety of common ground definitions and modeling approaches we started by collecting a list of all papers published in the acl anthology since 2015 that mention "common ground" or "grounding" in their abstracts together with "dialogue", and then examined them in terms of the definition of common ground and which approaches were used to model it. we started with 448 papers and focused on those that address different dimensions of common ground relevant for dialogue processing. specifically, we focused on the papers that discuss modality (e.g., textual, visual, multimodal grounding), type (static vs. dynamic), and scope of grounding (commonsense, domain or contextual knowledge). some papers were removed from the initial selection if their content was deemed irrelevant and some papers were added based on citation chaining. according to the classification of literature reviews outlined in paré and kitsiou @xcite , this survey can thus be considered as both descriptive and narrative since it aims to provide an overview of the available work and identify some trends for grounding in dialogue. simultaneously, it is more focused on a qualitative interpretation of prior knowledge. the survey was conducted by a small team of researchers with experience in natural language processing who selected and judged papers independently. the inclusion of papers in the survey was determined based on the relevance of their topics to both grounding and dialogue as reflected in the abstract and assessed by the authors (see appendix a for more detail and statistics). figure 1 shows a word cloud based on the word frequencies of the most common terms from the abstracts of the surveyed papers. unsurprisingly, dialogue, model, and knowledge are the most frequent terms; although terms such as generation, context, visual, and multimodal are also commonly used, which reflects the challenging and multifaceted nature of common ground research. ## common ground dimensions common ground contributes to successful communication through dialogue. the concept is thereby easy to define in terms of its relevance, but what information is needed to form common ground is fleeting. as noted by chandu et al. @xcite , there is often no such thing as an "axiomatic common ground". successful grounding in human-machine-communication is usually only evident in whether the goal of the task is reached; or through assessment of the quality of the conversation. while the common ground does consist of prior knowledge (such as world, commonsense or knowledge about previous events), much of the common ground of a conversation is built and established during communication. a system must thereby adapt to the evolving context of the conversation and the newly acquired knowledge. there is not one way to establish a grounded dialogue between human and machine. as chandu et al. @xcite note, grounding is often performed with the goal of supporting a more defined end purpose task. how grounding is achieved heavily depends on the purpose of the conversation, e.g., whether the goal is to find a an object in a shared environment or to enrich a dull chit-chat with more interesting or personal, user-targeted facts. researchers have proposed different methods for establishing common ground up to now. we aim to systematize the existing approaches to symbol grounding and categorize them in terms of the following dimensions (see figure 2 ): ## common ground in lms effective conversation requires building common ground that is ideally multimodal, dynamic, integrates commonsense and contextual knowledge. however, in the modern age of large lms (llms) common ground is typically defined by the user or it is based on the static training data. for instance, prompts can be used to include persona, domain and task information that is supposed to represent shared knowledge, but this information is tailored in a way that reflects the user's point of view. moreover, inputs to llms are usually based on text even when they reference other modalities (e.g. describing location, time or emotions). llm outputs may also contain hallucinations or incorrect assumptions. jiang et al. @xcite distinguish between two potential sources of factual hallucinations: insufficient knowledge within the model's parameters and knowledge memorization coupled with the lack of generalization capability. however, some hallucinations can also be a product of miscommunication caused by the lack of common ground: shaikh et al. @xcite show that llms tend to generate text with less conversational grounding (on average, 77.5% less likely compared to humans) and often presume common ground instead of building it incrementally over time. language models often exhibit reduced rates of grounding acts and show poor grounding agreement with humans. existing supervised fine-tuning and preference optimization datasets are potential sources of this problem @xcite , because such datasets are meant for training models that simply "follow" instructions based on a limited number of interactions. models trained on such data never learn how to build common ground throughout the conversation and adjust it depending on new inputs. jokinen @xcite also emphasize the need for grounding in lm-based dialogue systems and argue that such systems need to build a shared understanding of dialogue context and intents, grounding generated utterances in real-world events and potentially bridging the gap between neural and symbolic computing. furthermore, schneider et al. @xcite benchmark both open-and closed-source lms on the grounding tasks and find that both are equally good at classifying grounding acts but identifying grounded knowledge proved to be very challenging and it is better handled by close-source lms. another important issue is that llm-based conversational agents may fail to generate safe and appropriate responses @xcite and often go along with a problematic user input, generating offensive and toxic language. kim et al. @xcite aim to address this issue by proposing groundial that grounds dialog responses in commonsense social rules and does not require any additional fine-tuning. groundial combines in-context learning with guided decoding that follows human norms to generate more safe and appropriate responses. yu et al. @xcite emphasize that commonly used techniques for dialogue response generation are based on chain-of-thought (cot) @xcite or retrieval augmented generation (rag) @xcite . however, both methods have important drawbacks. on the one hand, cot may overestimate the capabilities of llms by treating them as isolated knowledge sources, while the knowledge stored in llms can be outdated, and llms are prone to hallucinations @xcite . on the other hand, approaches like rag underplay the internalized knowledge of llms and mostly rely on external sources. yu et al. @xcite propose a different approach that considers llm as a collaborator and includes several thought-then-generate stages to identify knowledge demands and then find relevant information via demands-guided knowledge retrieval. chiu et al. @xcite draw attention to another limitation of llms in the context of grounded task-oriented dialogue: llms are difficult to steer towards task objectives and they have difficulties with handling novel grounding. to address these limitations chiu et al. @xcite propose an interpretable grounded dialogue system that combines llms with a symbolic planner to perform grounded code execution and response generation. the proposed system has a reader that uses llm to convert utterances into executable code functions which represent the core meaning and map language to symbolic actions, and a symbolic planner that can plan over the symbolic actions and determine the next response. the task progress is tracked via bayesian reasoning and information gain objective. this approach achieved promising results on the onecommon task from @xcite that involves collaborative reference resolution. factual inconsistency of llms @xcite is another important concern for building a conversational agent. previous work has shown that llms can generate factually incorrect responses even when provided with valid knowledge sources @xcite . xue et al. @xcite tackle the inconsistency issue in knowledge-grounded conversations by enhancing the factual knowledge expression via extended feed forward networks (ffn) in transformers and apply reinforcement learning that implicitly aligns dialogue responses with gold knowledge using factual consistency preference. the parameters in extended ffns are updated based on the knowledge-related tokens that appear in both grounding knowledge and response, e.g., if the term "argentina" is part of the response it can be enhanced with the factual knowledge "argentina won the 2022 world cup champion." daheim et al. @xcite also investigate factuality in the context of document-grounded dialogue generation and consider two components: a simple ungrounded response generation model that encourages fluency, and a component that encourages responses which can help reconstruct the response grounding document. the proposed method uses bayes' theorem to decompose the posterior distribution of a response given context and grounding into these components, and employs scaling factors to promote either greater correctness or fluency of the response. although this approach results in improved factuality, the online decoding is computationally demanding. mohapatra et al. @xcite provide a benchmark and design various tests to assess how well llms handle grounding as both speakers and listeners. as listeners, llms should integrate repaired or canceled information and identify ambiguous cases that need clarification. as speakers, they must generate accurate and unambiguous responses. mohapatra et al. @xcite analyze perplexity on the responses that are appropriate and grounded and responses that are fitting but contextually incorrect. they found a strong correlation between conversational abilities and the size of the models and the pre-training data with larger models and datasets leading to improved grounding capabilities and lower perplexity for correct responses. overall, building common ground with lms is a very challenging task and the following issues that can hinder successful communication need to be taken into consideration: in order to visualize the distribution of topics that are most prominent in the current research on dialogue grounding, we applied k-means clustering to the abstracts of 448 papers selected for the survey based on keyword match. we then categorized them according to common topics based on the top 25 words per cluster (after stopword removal). figure 3 shows that almost 16% of all papers focus on grounded generation and selection, while 15.2% of the papers are concerned with cognitive and human-centered aspects of grounding. knowledge grounding and llm fine-tuning are two other relatively big topics (13% each). interestingly, multimodal papers represent only 7.4% with a similar number of papers dedicated to learning-based approaches. multimodal papers with emphasis on spatial grounding are even less prominent (only 5.37%), and, quite worryingly, only 2% of all publications focus on evaluation and benchmarking. these trends show that there are some considerable gaps in the current research on conversational grounding. as demonstrated in this survey, well-grounded dialogue requires some commonsense knowledge as well as contextual knowledge that heavily depends on the dialogue history and previous interactions, and it may also include some domain-specific knowledge that needs to be derived based on the shared environment, experiences and expectations. moreover, we believe that the interdisciplinary collaboration between different fields such as robotics, cognitive science and linguistics will greatly benefit this research area. there is also a need for more diverse and realistic datasets that are not purely text-based or combine text with just one modality e.g. images, the community needs a collection of various types of data that capture different dimensions of grounding with respect to modality, type and scope. more often than not research on grounding addresses the static setting which assumes that we have an access to some graph or a knowledge base, but grounding is a collaborative process and more research needs to be done on incorporating dynamic features. another important aspect that has only recently started gaining more attention is evaluation. for instance, alghisi et al. @xcite investigate the impact of incorporating external knowledge to ground dialogues with retrieval-augmented generation or gold knowledge and emphasize the importance of human evaluation. chaudhary et al. @xcite find that llm-based evaluation does not align well with human judgments and show that evaluation results are not robust against perturbations. ghaddar et al. @xcite argue that knowledge-grounded dialogue needs to be more thoroughly evaluated with respect to hallucinations. an important future avenue is research comparing common ground in human-human and human-robot interactions. for both, there is a need to collect more diverse data and combine llm-based generation with neuro-symbolic approaches. for instance, bonial et al. @xcite use an abstract meaning representation formalism to ground language concepts in the robot's world model, and torres-foncesca et al. @xcite investigate an important dimension of knowledge grounding related to object permanence, i.e., the ability to maintain mental representations of objects even when they are not in view. more research should be done towards integrating common ground during pre-training and fine-tuning stages of llms, e.g., by introducing additional loss functions and contrastive learning to distinguish between compatible and incompatible beliefs being formed in a conversation (or by measuring perplexity between correct and adversarial responses @xcite and combining generation with grounding reconstruction @xcite ). some recent works explore how one can build a knowledge-grounded dialog system that utilizes both dialog history and local knowledge base for response generation with a semi-supervised pre-training @xcite and perform large-scale multi-party aware pre-training on conversational data @xcite that shows promising results for knowledge grounded conversations. ## conclusion in this survey we provided an overview of different dimensions of common ground and categorized them according to the modality, type and scope. we also discussed existing modeling approaches for knowledge-based, visual, and persona-based grounding, exemplifying promising research directions and attempts to integrate various aspects of common ground. we talked about leveraging common ground in llms, and summarized the issues related to the lack of conversational grounding in such models. we also described current and promising future research directions. we hope that this survey and our annotations foot_0 will serve as a guide for exploring the broad and dynamic landscape of conversational grounding.