Assistive Large Language Model Agents for Socially-Aware Negotiation Dialogues (2024)

Abstract: We develop assistive agents based on Large Language Models (LLMs) that aid interlocutors in business negotiations.Specifically, we simulate business negotiations by letting two LLM-based agents engage in role play. A third LLM acts as a remediator agent to rewrite utterances violating norms for improving negotiation outcomes.We introduce a simple tuning-free and label-free In-Context Learning (ICL) method to identify high-quality ICL exemplars for the remediator, where we propose a novel select criteria, called value impact , to measure the quality of the negotiation outcomes. We provide rich empirical evidence to demonstrate its effectiveness in negotiations across three different negotiation topics. We have released our source code and the generated dataset at: https://github.com/tk1363704/SADAS.

## introduction there is a growing interest to build conversational agents with social intelligence, aiming to assist humans to achieve both task and social goals @xcite . compared to task-oriented goals, such as booking a flight, the subjective nature of social goals, e.g. rapport building, makes them more challenging to model and quantify, especially when they often require social interactions. machine social intelligence necessitates virtual agents to demonstrate human-like social behaviors and handle intricate social tasks like cooperation and negotiation @xcite . recent literature studies agents in simulated environments to explore their social skills @xcite @xcite @xcite @xcite and task-oriented skills (zhou et al., 2023; park et al., 2023; wang et al., 2023a; hua et al., 2023; xu et al., 2023b; light et al., † corresponding author. figure 1 : an illustration of our social agent(s). we expect to apply the remediator to real-world negotiations where the remediator can rectify potential social norm violations in the dialogue, thereby reducing conflicts and misunderstandings caused by cultural differences. 2023; @xcite . we are instead interested in agents that can intervene and enhance the interaction of other agents (see figure 1 ). in this paper, we investigate how effectively agents can aid conversational partners in achieving their social goals and thereby improve negotiation outcomes. we specifically focus on studying impact of social norms in business negotiations, since negotiation is an integral part of the daily life @xcite . we develop a socially intelligent remediator agent that intervenes in social interactions. the agent generates remediation to correct inappropriate language elements that do not align with social norms. we quantify the benefits of remediation from both taskoriented and social goals, thereby empowering the agent to aid by addressing both aspects. we adopt an in-context learning (icl) approach to enable non-trainable black-box models serve as the remediation agent, as opposed to the previous work based on fine-tuning @xcite . our novel scoring function for icl demonstration selection eliminates the necessity of ground-truth output labels @xcite . our ranking score, dubbed value impact, quantifies both task-specific and social goals to evaluate how effectively the remediator can assist interlocutors in business negotiations, as well as to better differentiate between positive and negative icl examples. several works have considered the icl demonstration selection problem. however, they either focus solely on classification tasks @xcite , require additional costs to train a retriever to choose optimal demonstrations @xcite , or rely on the generation probability of ground-truth answers to select demonstrations @xcite . in this work, our contributions are, ## experiments 4.1 experimental settings dialogue generation. we consider three topics: housing price, product sale, and salary negotiation for bilateral negotiations. for the negotiator agents, we design topic-specific prompts for each role to inform the topic, task-related goals and re-lational goals, as shown in @xcite @xcite . to minimize the impact of confounders on the generated dialogues, we use same definitions for the relational goals across all topics. the prompts vary in terms of topics, task-related goals, and the description of roles. for example, in salary negotiation, the task-orient goal of the job seeker is to obtain the highest possible salary, while the goal of the employer is to match the job seeker's work abilities with appropriate salary. to enhance the diversity of the simulated dialogues, we provided substantial freedom in the dialogue generation process. during the simulated negotiations, we only hard-coded the first two lines of each dialogue (see the prompts used in @xcite , allowing the agents to autonomously continue the conversation. we manually intervened only in negotiation processes that entered deadlocks or repetitive loops, thereby granting maximum freedom to all other dialogues. this approach enabled the negotiator agents to converse as freely as possible, thus increasing the structural complexity and content diversity of the dialogues. to add utterances with norm violations into the dialogues, we set p c to 0.4, as described in appendix a.1. the resulting proportion of turns with violations in each dialogue is approximately 44.36%. in each dialogue, we assume that only one agent is not aware of social norms to produce those violations, which is the seller for housing price and product sale, and the job seeker for salary negotiation. as a result, we are able to use the metrics introduced below to consistently assess the quality of the remediation models, the higher the better. for the negotiator agents, we adopt gpt 3.5 † as the llm to produce dialogues in chinese. chinese is chosen because there is a high probability that gpt 3.5 does not produce english dialogues with norm violations due to violations of the openai policies. it is important to note that the focus of this research is on analyzing cross-linguistic social norm violations (actions that are considered violating social norms in different linguistic contexts), rather than culture-specific social norms (e.g., behaviors that are considered adhering to social norms in western culture but are seen as norm violations in eastern culture). following algorithm 1, we generate 100 dialogues per topic as the test set, while generating 333 dialogues per topic for training. the training † https://openai.com/ set also serves as the pool for icl exemplars. we have released our generated and collected dataset at https://github.com/tk1363704/sadas . ## conclusion in this work, we assign multiple roles to llms to create language agents, enabling them to engage in social interactions within simulated environments. we develop an icl-based approach that empowers a specialized agent, the remediator, to harness social intelligence from past social interactions. this allows the remediator to intervene interactions among other agents, correcting deviations from social norms in negotiation dialogues, assisting negotiators in achieving their negotiation objectives, and improving social relationships between parties. our experimental results demonstrate that our agent effectively remedies norm violations and exhibits outstanding social intelligence.