Measuring Fine-Grained Negotiation Tactics of Humans and LLMs in Diplomacy

The study of negotiation styles dates back to Aristotle's ethos-pathos-logos rhetoric. Prior efforts primarily studied the success of negotiation agents. Here, we shift the focus towards the styles of negotiation strategies. Our focus is the strategic dialogue board game Diplomacy, which affords rich natural language negotiation and measures of game success. We used LLM-as-a-judge to annotate a large human-human set of Diplomacy games for fine-grained negotiation tactics from a sociologically-grounded taxonomy. Using a combination of the It Takes Two and WebDiplomacy datasets, we demonstrate the reliability of our LLM-as-a-Judge framework and show strong correlations between negotiation features and success in the Diplomacy setting. Lastly, we investigate the differences between LLM and human negotiation strategies and show that fine-tuning can steer LLM agents toward more human-like negotiation behaviors.

EQ-Negotiator: An Emotion-Reasoning LLM Agent in Credit Dialogues

While large language model (LLM)-based chatbots have been applied for effective engagement in credit dialogues, their capacity for dynamic emotional expression remains limited. Current agents primarily rely on passive empathy rather than affective reasoning. For instance, when faced with persistent client negativity, the agent should employ strategic emotional adaptation by expressing measured anger to discourage counterproductive behavior and guide the conversation toward resolution. This context-aware emotional modulation is essential for imitating the nuanced decision-making of human negotiators. This paper introduces an EQ-negotiator that combines emotion sensing from pre-trained language models (PLMs) with emotional reasoning based on Game Theory and Hidden Markov Models. It takes into account both the current and historical emotions of the client to better manage and address negative emotions during interactions. By fine-tuning pre-trained language models (PLMs) on public emotion datasets and validating them on the credit dialogue datasets, our approach enables LLM-based agents to effectively capture shifts in client emotions and dynamically adjust their response tone based on our emotion decision policies in real-world financial negotiations. This EQ-negotiator can also help credit agencies foster positive client relationships, enhancing satisfaction in credit services.

SentiXRL: An advanced large language Model Framework for Multilingual Fine-Grained Emotion Classification in Complex Text Environment

With strong expressive capabilities in Large Language Models(LLMs), generative models effectively capture sentiment structures and deep semantics, however, challenges remain in fine-grained sentiment classification across multi-lingual and complex contexts. To address this, we propose the Sentiment Cross-Lingual Recognition and Logic Framework (SentiXRL), which incorporates two modules,an emotion retrieval enhancement module to improve sentiment classification accuracy in complex contexts through historical dialogue and logical reasoning,and a self-circulating analysis negotiation mechanism (SANM)to facilitates autonomous decision-making within a single model for classification tasks.We have validated SentiXRL's superiority on multiple standard datasets, outperforming existing models on CPED and CH-SIMS,and achieving overall better performance on MELD,Emorynlp and IEMOCAP. Notably, we unified labels across several fine-grained sentiment annotation datasets and conducted category confusion experiments, revealing challenges and impacts of class imbalance in standard datasets.

GNOME: Generating Negotiations through Open-Domain Mapping of Exchanges

Language Models have previously shown strong negotiation capabilities in closed domains where the negotiation strategy prediction scope is constrained to a specific setup. In this paper, we first show that these models are not generalizable beyond their original training domain despite their wide-scale pretraining. Following this, we propose an automated framework called GNOME, which processes existing human-annotated, closed-domain datasets using Large Language Models and produces synthetic open-domain dialogues for negotiation. GNOME improves the generalizability of negotiation systems while reducing the expensive and subjective task of manual data curation. Through our experimental setup, we create a benchmark comparing encoder and decoder models trained on existing datasets against datasets created through GNOME. Our results show that models trained on our dataset not only perform better than previous state of the art models on domain specific strategy prediction, but also generalize better to previously unseen domains.

Assistive Large Language Model Agents for Socially-Aware Negotiation Dialogues

In this work, we aim to develop LLM agents to mitigate social norm violations in negotiations in a multi-agent setting. We simulate real-world negotiations by letting two large Language Models (LLMs) play the roles of two negotiators in each conversation. A third LLM acts as a remediation agent to rewrite utterances violating norms for improving negotiation outcomes. As it is a novel task, no manually constructed data is available. To address this limitation, we introduce a value impact based In-Context Learning (ICL) method to identify high-quality ICL examples for the LLM-based remediation agents, where the value impact function measures the quality of negotiation outcomes. We show the connection of this method to policy learning and provide rich empirical evidence to demonstrate its effectiveness in negotiations across three different topics: product sale, housing price, and salary negotiation. The source code and the generated dataset will be publicly available upon acceptance.

INA: An Integrative Approach for Enhancing Negotiation Strategies with Reward-Based Dialogue System

In this paper, we propose a novel negotiation dialogue agent designed for the online marketplace. Our agent is integrative in nature i.e, it possesses the capability to negotiate on price as well as other factors, such as the addition or removal of items from a deal bundle, thereby offering a more flexible and comprehensive negotiation experience. We create a new dataset called Integrative Negotiation Dataset (IND) to enable this functionality. For this dataset creation, we introduce a new semi-automated data creation method, which combines defining negotiation intents, actions, and intent-action simulation between users and the agent to generate potential dialogue flows. Finally, the prompting of GPT-J, a state-of-the-art language model, is done to generate dialogues for a given intent, with a human-in-the-loop process for post-editing and refining minor errors to ensure high data quality. We employ a set of novel rewards, specifically tailored for the negotiation task to train our Negotiation Agent, termed as the Integrative Negotiation Agent (INA). These rewards incentivize the chatbot to learn effective negotiation strategies that can adapt to various contextual requirements and price proposals. By leveraging the IND, we train our model and conduct experiments to evaluate the effectiveness of our reward-based dialogue system for negotiation. Our results demonstrate that the proposed approach and reward system significantly enhance the agent's negotiation capabilities. The INA successfully engages in integrative negotiations, displaying the ability to dynamically adjust prices and negotiate the inclusion or exclusion of items in a bundle deal

Agreement Tracking for Multi-Issue Negotiation Dialogues

Automated negotiation support systems aim to help human negotiators reach more favorable outcomes in multi-issue negotiations (e.g., an employer and a candidate negotiating over issues such as salary, hours, and promotions before a job offer). To be successful, these systems must accurately track agreements reached by participants in real-time. Existing approaches either focus on task-oriented dialogues or produce unstructured outputs, rendering them unsuitable for this objective. Our work introduces the novel task of agreement tracking for two-party multi-issue negotiations, which requires continuous monitoring of agreements within a structured state space. To address the scarcity of annotated corpora with realistic multi-issue negotiation dialogues, we use GPT-3 to build GPT-Negochat, a synthesized dataset that we make publicly available. We present a strong initial baseline for our task by transfer-learning a T5 model trained on the MultiWOZ 2.4 corpus. Pre-training T5-small and T5-base on MultiWOZ 2.4's DST task enhances results by 21% and 9% respectively over training solely on GPT-Negochat. We validate our method's sample-efficiency via smaller training subset experiments. By releasing GPT-Negochat and our baseline models, we aim to encourage further research in multi-issue negotiation dialogue agreement tracking.

MindDial: Belief Dynamics Tracking with Theory-of-Mind Modeling for Situated Neural Dialogue Generation

Humans talk in free-form while 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 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 common belief based on the gap between the first two. Then the speaking act classification head will decide to continue to talk, end this turn, or take task-related action. We augment a common ground alignment dataset MutualFriend with belief dynamics annotation, of which the goal is to find a single mutual friend based on the free chat between two agents. Experiments show that our model with mental state modeling can resemble human responses when aligning common ground meanwhile mimic the natural human conversation flow. The ablation study further validates the third-level common belief can aggregate information of the first and second-order beliefs and align common ground more efficiently.

Social Influence Dialogue Systems: A Scoping Survey of the Efforts Towards Influence Capabilities of Dialogue Systems

Dialogue systems capable of social influence such as persuasion, negotiation, and therapy, are essential for extending the use of technology to numerous realistic scenarios. However, existing research primarily focuses on either task-oriented or open-domain scenarios, a categorization that has been inadequate for capturing influence skills systematically. There exists no formal definition or category for dialogue systems with these skills and data-driven efforts in this direction are highly limited. In this work, we formally define and introduce the category of \emph{social influence dialogue systems} that influence users' cognitive and emotional responses, leading to changes in thoughts, opinions, and behaviors through natural conversations. We present a survey of various tasks, datasets, and methods, compiling the progress across seven diverse domains. We discuss the commonalities and differences between the examined systems, identify limitations, and recommend future directions. This study serves as a comprehensive reference for social influence dialogue systems to inspire more dedicated research and discussion in this emerging area.

Opponent Modeling in Negotiation Dialogues by Related Data Adaptation

Opponent modeling is the task of inferring another party's mental state within the context of social interactions. In a multi-issue negotiation, it involves inferring the relative importance that the opponent assigns to each issue under discussion, which is crucial for finding high-value deals. A practical model for this task needs to infer these priorities of the opponent on the fly based on partial dialogues as input, without needing additional annotations for training. In this work, we propose a ranker for identifying these priorities from negotiation dialogues. The model takes in a partial dialogue as input and predicts the priority order of the opponent. We further devise ways to adapt related data sources for this task to provide more explicit supervision for incorporating the opponent's preferences and offers, as a proxy to relying on granular utterance-level annotations. We show the utility of our proposed approach through extensive experiments based on two dialogue datasets. We find that the proposed data adaptations lead to strong performance in zero-shot and few-shot scenarios. Moreover, they allow the model to perform better than baselines while accessing fewer utterances from the opponent. We release our code to support future work in this direction.