M ind D ial: Enhancing Conversational Agents with Theory-of-Mind for Common Ground Alignment and Negotiation (2024)

Abstract: Humans talk in daily conversations while aligning and negotiating the expressed meanings or common ground. Despite the impressive conversational abilities of the large generative language models, they do not consider the individual differences in contextual understanding in a shared situated environment. In this work, we propose MindDial, a novel conversational framework that can generate situated free-form responses to align and negotiate common ground. We design an explicit mind module that can track three-level beliefs – the speaker’s belief, the speaker’s prediction of the listener’s belief, and the belief gap between the first two. Then the next response is generated to resolve the belief difference and take task-related action. Our framework is applied to both prompting and fine-tuning-based models, and is evaluated across scenarios involving both common ground alignment and negotiation. Experiments show that models with mind modeling can generate more human-like responses when aligning and negotiating common ground. The ablation study further validates the three-level belief design can aggregate information and improve task outcomes in both cooperative and negotiating settings.

## introduction we align and negotiate our common ground every day in daily chit-chat @xcite . in a common ground alignment scenario, agents are talking toward a joint goal, topics ranging from daily trivia to important multi-party meetings. in common ground negotiation situations, two parties resolve the differences in their beliefs, intents, or goals in a way that both find acceptable, such as item trading and discussing job offers @xcite . though it seems easy between human conversations, it requires complicated social capabilities. importantly, for all types of human communication including language, the relationship between correspondence to zilong @xmath0.ai>. the overt communicative act and common ground -of whatever type -is complementary. that is, as more can be assumed to be shared between communicator and recipient, less needs to be overtly expressed @xcite . taking figure 1b as an example, when bob asks about "joe davis", alice will align the precise referents of the query by keeping "joe" but correct "davis" to "smith". in this process, people need to realize what is shared and what needs to be further aligned or negotiatedwhich requires the understanding between points of view from their own and others' perspectives @xcite -the cognitive capability known as theory-of-mind (tom). the recent surge of large language models (llms) @xcite have dominated the natural language processing (nlp) community for their prominent natural language generation performance. although llms have shown their potential in tom benchmarks @xcite @xcite , applying tom for situated dialogue generation remains underexplored. in these situated tasks, agents' interactions are influenced by the environment, their shared experiences, and immediate goals. the participants need to take into account not only the linguistic content but also factors such as the social context, prior knowledge, and each other's beliefs. without tom, the models can only provide the most possible response as a one-turn question-answering as shown in figure 1a . to enable llms to interact with people in a more socially realistic manner, it is essential to incorporate tom for various forms of communications, such as aligning and negotiating common ground within dialogues @xcite @xcite . in this work, we introduce minddial, a new dialogue framework designed to facilitate the alignment and negotiation of common ground in situated dialogues, incorporating tom modeling. in-figure 1 : left: single-turn question answering. right: multi-turn common ground alignment. speakers will update their belief estimation based on context and generate the next response to reduce the belief differences. in sum, we consider our contributions as threefold: ## related work theory-of-mind (tom) tom is a crucial capability for human social interactions developed in early life @xcite . in literature, early works model belief update through time in sequential games with partially observable markov decision process (pomdp) @xcite @xcite @xcite . one agent's belief update is based on the estimate of others' current beliefs, resulting in an infinite recursion. however, in real life, studies have shown that humans could go no deeper than two levels of recursion @xcite . therefore, works @xcite began the efforts to end the recursion when their beliefs merge into the "common mind". modeling the belief of others has been extensively studied in symbolic-like environments @xcite @xcite , where agents need to incorporate or compete for a goal. efforts to measure models' ability to recognize false beliefs and perspective-taking also emerge in robotics @xcite , computer vision @xcite , and natural language processing @xcite using the sally-anne test @xcite . different variants of the sally-anne test and tom benchmarks are also proposed to test the tom of large language models @xcite @xcite . it is also shown that augmenting the model with external mind modules can help improve the performance of tasks involving intensive belief exchange and rich social interaction scenarios @xcite @xcite . in this work, we explore tom modeling can enhance the quality and efficiency of the re-sponse generation in both cooperative and semicooperative dialogue tasks. common ground alignment and negotiation in a cooperative dialogue task, to guarantee that the communication takes the least cost meanwhile providing the most informative messages, previous works proposed multiple methods to align the common ground between agents @xcite . specifically for dialogue tasks, datasets have been collected to provide golden utterances when people try to align the common ground with each other based on structured knowledge @xcite , in partially observable cooperative tasks @xcite , in multimodal and continuous environment @xcite . frameworks have been adopted to model and predict the aligning dynamics using gnn, rnn, transformers, and llms @xcite @xcite @xcite . the inferred common ground is also used to generate more interesting and engaging conversations for the dialogue agents @xcite . negotiation is treated as a semi-cooperative task since agents can have different goals but need to agree on the same decision @xcite . it requires complex social skills and strategies like offering proposals and accepting or making counteroffers @xcite . to improve the negotiating abilities of the dialogue systems, datasets of open-domain human negotiation corpus have been introduced in embodied environment @xcite , daily items split @xcite , buy and sell @xcite , job offer negotiation @xcite . modeling begins with game theory and action selection @xcite . for open-domain generation, methods have been designed to help the model plan ahead @xcite , give feedback about the current conversation @xcite , detect negotiation breakdowns @xcite . ## experiments dataset to provide a reasonable quantitative measure of belief dynamics in the dialogue, the expected dataset should contain rich belief exchanges. meanwhile, the belief exchange and the final solution can be easily labeled. therefore, we choose two representative settings to evaluate our framework. mutualfriend @xcite ): we consider it as an alignment dialogue scenario. in the mutualfriend task, each agent has a private knowledge base including a list of friends and their attributes like name, school, etc. there is a shared friend that both agents have and they need to chat with each other to find this mutual friend. we only keep the successful dialogues and the final data split for train/val/test is 7257/878/900. each dialogue in the training set contains a maximum of 53 turns and each turn with a maximum length of 29. casino @xcite : we consider it as the negotiating scenario. in the casino task, two agents need to split camping packages: 3 water, 3 firewood, and 3 food. each of these items will be of either high, medium, or low value to each agent. the agents need to negotiate the distribution of the items through chat to maximize their final points calculated based on the number of items they get and the items' corresponding values. the data split for train/val/test is 900/30/100. each dialogue in the training set contains a maximum of 27 turns and each turn with a maximum length of 106. mind modules to serve as a mind module in this task, the model is expected to understand long conversation contexts and the concept of first and second-order beliefs. we choose llama-2-7bchat, llama-2-13b-chat (touvron et al., 2023) foot_0 , gpt-3.5, and gpt-4 foot_1 as our mind reasoner for their potentials in tom benchmarks and the flexible abilities of mind reasoning in open-domain dialogues. response generators we adopt the same four models in the mind modules as our response generators. we divide the models into two groups: finetuning and prompting-based. for the finetuning group, we first finetune llama-2-7b-chat and llama-2-13b-chat to generate the next response with the raw training dialogues. then, we sample 3% of the training data and predict the first and second-order beliefs at each turn using the mind module, which are put into the dialogue context as additional information input to finetune the model again. we choose to combine only a small portion of training data input with beliefs to reduce the api query cost. we also vary the portion to 1%, 3%, and 5%. the sample size does not significantly influence the model performance (see appendix e). for gpt-3.5 and gpt-4, we use prompts to regulate the conversation. for finetuning-based models, the models are trained on two a6000 gpus for one epoch with an initial learning rate of 1e-4. the batch size is set to 64. for prompting-based models, we use the openai api for experiments. ## conclusion in this study, we present minddial, a novel framework for generating situated dialogue responses for common ground alignment and negotiation. by incorporating the first-and second-order tom modeling into account, our model can enhance the alignment accuracy and negotiation outcome in both finetuning and prompting-based models. the efficacy of our approach is further substantiated through ablation studies and user feedback.