We Argue to Agree: Towards Personality-Driven Argumentation-Based Negotiation Dialogue Systems for Tourism (2025)

Abstract: Integrating argumentation mechanisms into negotiation dialogue systems improves conflict resolution through exchanges of arguments and critiques. Moreover, incorporating personality attributes enhances adaptability by aligning interactions with individuals’ preferences and styles. To advance these capabilities in negotiation dialogue systems, we propose a novel Personality-driven Argumentation-based Negotiation Dialogue Generation (PAN-DG) task. To support this task, we introduce PACT, a dataset of Personality-driven Argumentation-based negotiation Conversations for Tourism sector. This dataset, generated using Large Language Models (LLMs), features three distinct personality profiles, viz. Argumentation Profile, Preference Profile, and Buying Style Profile to simulate a variety of negotiation scenarios involving diverse personalities. Thorough automatic and manual assessments indicate high-quality dialogues in the dataset. Further, we conduct comparative experiments between pre-trained and fine-tuned LLMs for the PAN-DG task. Multi-dimensional evaluation demonstrates that the fine-tuned LLMs effectively generate personality-driven rational responses during negotiations. This underscores effectiveness of PACT in enhancing personalization and reasoning capabilities in negotiation dialogue systems, thereby establishing a foundation for future research in this domain.

## introduction the rapid growth of the tourism industry (wttc, 2024) demands advanced negotiation dialogue systems to streamline trip booking and enhance traveler experiences. a key challenge lies in effective planning and decision-making, where llm-based agents can contribute significantly. unlike simple * equal contribution 1 resources can be accessed at https://www.iitp.ac. in/~ai-nlp-ml/resources.html ; https://github.com/ priyanshu-profile/pact/ . i love seeing famous sites, but i'm also drawn to beautiful views or unique events. i want a package that mixes popular attractions with hidden gems and spontaneous adventures. i don't mind going off the beaten path. i have a great package that seems like a perfect fit for you! it includes comfortable accommodations, premium dining options, and private transportation. the total price is @xmath053,788. activities range from vr experiences to outdoor adventures, detox sessions, and mindfulness. what do you think? you seem like an adventurous traveler who enjoys exploring both famous sites and hidden gems. i recommend the sighttour package, which includes guided tours of renowned landmarks alongside exclusive off-the-beaten-path experiences. the total price is @xmath160,000, but we have a special rate of $53,788. also, booking now ensures priority access to exclusive events. how does that sound? transactional tasks, tourism negotiations involve diverse user preferences, conflicting constraints, and planning across interdependent variables (e.g., price, accommodation, destination), areas where current llms often fall short @xcite . argumentation-based negotiation (abn) @xcite addresses these complexities by enabling the exchange of meta-information like justifications and persuasive arguments (dimopoulos and moraitis, 2014). the tourism domain, with its complex and personalized decisions, serves as an ideal testbed for evaluating llms and abn's effectiveness in achieving collaborative outcomes. a robust abn dialogue system for tourism enriches insights into the negotiating parties' perspectives, ultimately fostering a more informed, transparent, and collaborative dialogue. personality is a crucial determinant in shaping negotiation outcomes as it significantly influences actions and behaviors of the negotiating parties @xcite . integrating personality information into abn systems further amplifies their potential to secure collaborative outcomes by tailoring arguments and justifications to align with the personal values and negotiation styles of the parties involved, thereby fostering mutual understanding and agreement. such personality-driven collaborative abn dialogues enable smoother, more engaging, and highly personalized interactions, which are essential for maintaining a positive negotiating environment. for instance, as illustrated in figure 1 , a generic response lacks both personalization and justification, thus can lead to traveler dissatisfaction and potential negotiation failure. personality-driven responses reflect traveler preferences but lack persuasive support, while abnspecific responses provide justification but remain impersonal. in contrast, personality-driven abnspecific responses integrate both personalization and persuasive reasoning (justify-price), leading to more engaging and effective negotiations. motivated by these considerations, we introduce a novel personality-driven argumentation-based negotiation dialogue generation (pan-dg) task aimed at generating negotiation dialogues tailored to individuals' personalities while integrating authoritative argumentation techniques for personalized, rational, and collaborative negotiation outcomes. the pan-dg task is structured to reflect real-life personalized negotiation scenarios and is divided into three core sub-tasks: (i) personality recognition, (ii) dialog act prediction, and (iii) response generation. these sub-tasks collectively address key phases of negotiation, from identifying negotiators' personalities, predicting next dialog act, to generating responses that align with these personalities and dialog acts. to support the pan-dg task, we present a novel dataset of personality-driven argumentation-based negotiation conversations for tourism, named pact. recognizing the labor and time-intensive nature of manually creating high-quality personalized conversational datasets @xcite @xcite , pact is automatically generated using llms, with human oversight through a three-stage pipeline: using this dataset, we conduct comprehensive comparative experiments involving pre-trained and fine-tuned llms for aforementioned sub-tasks, establishing strong baselines for future investigation. our dataset construction approach and task methodology are readily extensible to various llms, tasks, languages, and domains, offering a valuable tool for addressing challenges in data synthesis, eventually facilitating the development of more robust and scalable abn dialogue systems. in summary, our contributions are: (i) introduce a novel personality-driven argumentation-based negotiation dialogue generation task to generate dialogues aligned with negotiators' distinct personalities; (ii) develop an efficient multi-stage dataset generation pipeline using llms with human supervision to automate dataset creation; (iii) curate a new dataset, pact, comprising 8k+ personalitydriven argumentation-based negotiation conversations focused on tourism domain; (iv) define a rich set of three personality profiles: argumentation profile, preference profile, and buying style profile, capturing diverse negotiation behaviors; (v) establish strong llm-based baselines for three sub-tasks: personality recognition, dialog act prediction, and response generation, emulating reallife personalized negotiation scenarios. while these studies primarily model negotiation through the lens of game theory and action selection @xcite , our research takes a novel approach by adopting an argumentation-based framework for collaborative negotiation within the tourism domain. to our knowledge, this work pioneers the exploration of argumentation-based negotiation dialogue systems. ## pact dataset construction pipeline to develop the pact dataset with high-quality personality-driven argumentation-based negotiation conversations for tourism domain, we leverage the advanced capabilities of llms and design a dataset construction pipeline with three stages: (1) pact generation, (2) pact filtering, and (3) pact quality assessment, as shown in figure 2 . ## experiments tasks. upon the construction of pact, we can support an entire pan-dg task mirroring the real-life negotiation scenario. we split the entire personality-driven abn dialogue generation process into 3 sub-tasks: personality recognition (pr) that aims to identify the personality profiles of interlocutors through dialogue context; dialog act prediction (dap) that aims to infer the future dialog act for a given dialogue context; and response generation (rg) that aims to generate travel agents' probable response based on the dialogue context. to ensure factual consistency and mitigate hallucinations for rg task, we integrate background knowledge through a retrieval-augmented generation (rag) technique @xcite . specifically, we leverage the curated package database containing verified travel details, including a comprehensive list of amenities, services, and their corresponding pricing ( §3.1). further, to align responses with personality and dialogue act, we integrate outputs from the pr and dap sub-tasks. we use a multi-task learning (mtl) approach during training, where the model learns the relationships between personality, dialogue act, and response generation. during inference, pr and dap provide dynamic personality and dialog act information for contextually accurate, personality-driven response generation. we discuss rag and mtl in detail in appendix g.2. models. we evaluate 7 llms -llama models (llama-2-7b, llama-2-7b-chat-hf, llama-3-8b, llama-3.1-8b) @xcite , along with mistral-8b @xcite , vicuna-7b @xcite , and gpt-4.1-mini (openai, 2024), across three sub-tasks. task setups. following @xcite , we evaluate the effectiveness of pact on the pan-dg task under five setups: (i) pre-trained models (pms), (ii) pre-trained models with personality (pmswp), (iii) fine-tuning on a human-human negotiation dialogue dataset (ft-h2hnego), (iv) fine-tuning on pact (ft-vanilla), and (v) finetuning on pact with background knowledge, personality, and dialog act information (ft-ours). we follow a 70:20:10 train-validation-test split for all setups. setups details are given in appendix g.1. evaluation metrics. for pr and dap tasks, we use accuracy (acc) and macro f1 (f1). for rg task, we employ perplexity (ppl), bleu-1/2 (b-1/2), meteor (met), rouge-l (r-l), distinct-1/2 (d-1/2), bertscore-f1 (bs-f1), response length (r-len), personality accuracy (p-acc), and dialog act accuracy (d-acc). further, to measure faithfulness of generated response with dialogue context and package information, we employ feqa and questeval (qe). details on task setups, implementation specifications, and evaluation metrics are given in appendix g. ## evaluation results our proposed approach (ft-ours), which incorporates package knowledge along with personality and dialog act information, shows the highest improvements across all sub-tasks. notably, for rg task, it shows substantial improvements across multiple metrics, including response quality, dialogue coherence, and factual accuracy. a significant reduction in perplexity reflects enhanced model confidence and fluency. moreover, integrating personality and dialog act information results in increased p-acc and d-acc. specifically, compared to ft-vanilla counterpart, it obtains a 27.2% increase in p-acc and an 17.6% increase in d-acc for vicuna, highlighting the importance of these aspects. the notable improvements in feqa and qe scores further underscore enhanced factual grounding and response relevance. these results demonstrate that background package knowledge reduces the generation of generic responses while improving relevance and accuracy. furthermore, compared to chatgpt, vicuna under our proposed setup (ft-ours) achieves signifi-cantly better performance across all tasks, with a 20.4% increase in f1 for pr, 19.6% for dap, and 12.3% in bs-f1 for rg. despite chatgpt's strong general language capabilities, it underperforms on the pan-dg task, highlighting the necessity of domain-specific fine-tuning. the low scores on pr and dap suggest limited sensitivity to personality traits and dialog acts, which are critical for structured negotiation. its weaker rg performance further reflects challenges in generating contextually relevant, factual, dialog-act, and persona-driven responses. overall, these findings show that our dataset significantly improves the model's ability to generate responses that not only reflect personality but also align with structured dialog dynamics, thereby emphasizing the importance of structured dialog acts, personality adaptation, and knowledge enrichment in developing advanced, personalitydriven abn dialogue systems. human evaluations for rg are provided in appendix h. ## conclusion this work introduces a novel personality-driven argumentation-based negotiation dialogue generation (pan-dg) task, focused on generating personality-driven negotiation dialogues in the tourism domain. for pan-dg task, we curate pact dataset through a multi-stage pipeline using llms. pact integrates multi-dimensional personality profiles and abn-specific dialog acts to enhance personalized negotiation interactions. we also establish strong llm baselines for this task.