Personality-Driven Negotiation Dialogue Generation

• The paper introduces the PAN-DG framework that integrates structured argumentation moves with multidimensional personality profiles for adaptive negotiation dialogues.
• It employs a multi-task learning approach combining personality recognition, dialogue act prediction, and response generation, enhanced by retrieval-augmented techniques.
• Evaluation with the PACT dataset demonstrates significant improvements in personality consistency, negotiation effectiveness, and factual grounding.

Personality-driven Argumentation-based Negotiation Dialogue Generation (PAN-DG) refers to the task and methodology of synthesizing negotiation dialogues in which the agents not only engage in structured argumentation—presenting, justifying, or contesting offers—but also embody distinct, explicitly modeled personality profiles. This multifaceted paradigm unites rational negotiation strategies with personalization at the dialogue act and linguistic levels. PAN-DG systems are engineered to produce dialogues that are simultaneously rational (grounded in argumentation frameworks) and personalized (influenced by defined personality attributes), enabling more adaptive, persuasive, and human-compatible negotiation interactions.

1. PAN-DG Task Formulation and Motivation

PAN-DG is defined by its dual focus on (a) generating negotiation dialogues in which explicit argumentation acts drive the conversation, and (b) ensuring that responses, stances, and negotiation strategies are shaped by the participants' personality attributes (Priya et al., 14 Sep 2025). Each dialogue participant (e.g., a travel agent, traveler) is assigned multi-dimensional personality profiles that govern both their communicative style and negotiation tactics. The primary objectives include:

• Rationality: Dialogue content is constructed using explicit argumentation moves (e.g., “justify-price,” “negotiate-price-decrease/increase,” “ask-clarification”), which structure the flow of negotiation with logical moves and counter-moves.
• Personalization: Responses are personalized by encoding personalities along axes such as argumentation style (e.g., agreeable, argumentative), domain preference (e.g., “Beach Lover”), and economic bias (e.g., “budget-concerned”).
• Task Decomposition: The end-to-end PAN-DG process is decomposed into three interlinked subtasks:
  1. Personality Recognition (PR): Detecting and incorporating personality profiles from context.
  2. Dialogue Act Prediction (DAP): Inferring the next negotiation or argumentation move.
  3. Response Generation (RG): Generating contextually and personality-consistent responses.

PAN-DG targets real-world negotiation settings—such as tourism package negotiations—where negotiating agents must present rational offers, justify positions, adapt to counterarguments, and vary their approach based on personality traits (Priya et al., 14 Sep 2025).

2. Personality Modeling and Integration

PAN-DG leverages a multidimensional personality representation that encapsulates behavioral, preferential, and negotiation-style facets, going beyond generic traits to domain-relevant profiles:

Profile Dimension	Typical Values/Examples	Integration in Dialogue
Argumentation Profile	Agreeable, Disagreeable, Open-minded	Guides tone/concessions
Preference Profile	Beach Lover, Culture Creature	Informs offers/recommendations
Buying Style Profile	Budget/Quality Concerned, Balanced	Governs price/quality tradeoffs

The incorporation of such profiles involves explicit assignment at the start of a dialogue or dynamic inference as the conversation unfolds. For example, a “Disagreeable, Budget-concerned Beach Lover” will predictably push harder on price issues for beach packages and respond more assertively to counteroffers. The agent’s personality profile conditions both the choice of argumentation acts and the concrete response generation step (Priya et al., 14 Sep 2025).

Technical mechanisms for encoding and integrating personality include:

• Structured prompt templates that encode personality information alongside dialogue context and response expectations, guiding LLMs during generation.

• Retrieval-augmented generation (RAG) strategies, ensuring factual grounding while incorporating personality and dialog act signals.

• Multi-task learning setups where personality recognition, dialog act prediction, and response generation are trained jointly, reinforcing consistency between personality cues and argumentative behavior.

3. Dataset Construction: PACT

The PAN-DG paradigm necessitates datasets in which both negotiation moves and personality traits are explicitly annotated or constructed. The PACT dataset—Personality-driven Argumentation-based negotiation Conversations for Tourism—is specifically designed for this task (Priya et al., 14 Sep 2025). Key features include:

• Scale and Richness: Over 8,600 dialogues averaging ~21 turns each; conversations span a range of negotiation acts and argumentation moves.

• Annotation Structure:

  • Each dialogue is paired with explicit personality assignments for both agent roles along all three defined profile dimensions.
  • Dialog acts are labeled at the utterance level, distinguishing between argumentation (e.g., “Justify-price”) and negotiation (e.g., “Negotiate-add-X”) moves.
• Domain Grounding: Dialogues are anchored in realistic travel package data (amenities, prices, options) culled from an external database, adding factual diversity and negotiation depth.
• Quality Filtering: A “mixture-of-experts” pipeline employing auto-evaluation (BLEU, METEOR, ROUGE-L, DISTINCT, semantic entailment) and LLM-based expert filtering for coherence, factuality, negotiation quality, personality consistency, and toxicity.

This curated dataset supports targeted evaluation of model capabilities in encoding and expressing personality traits during realistic, multi-turn argumentation-based negotiations.

4. Methodological Innovations

PAN-DG models operationalize personality and argumentation via integrated architectural components and data workflows:

• Prompt and Template Design: Highly-structured prompts encompass the negotiation context, specific dialog acts, detailed personality cues, and factual anchors (e.g., travel package data) to condition LLM output. This contrasts with generic dialogue models, which lack such explicit organization.
• Argumentation-Driven Structure: Instead of relying on pure sequence-to-sequence autoregression, PAN-DG models employ dialog act prediction based on argumentation frameworks (e.g., when to “ask-clarification,” issue a “counter-argument,” or “justify” positions). Some systems employ LaTeX-formulated pricing equations (e.g., ℘ₐₖ = ℘ₜ₍ₖ₋₁₎ + (℘ₐ₍ₖ₋₁₎ – ℘ₜ₍ₖ₋₁₎)·e^–c·k) to guide rational concessions over negotiation rounds (Priya et al., 14 Sep 2025).
• Retrieval Augmentation and Fact Grounding: RAG modules fetch relevant package facts or background knowledge, reducing hallucination and increasing the negotiation’s authenticity.
• Multi-task Learning: Simultaneous optimization over personality recognition, dialog act prediction, and response generation increases cross-task consistency and model robustness.

5. Evaluation Protocols and Results

Assessment of PAN-DG models employs both automated metrics and human evaluations:

• Automated Metrics:
  • Fluency (Perplexity)
  • N-gram Overlap (BLEU-1/2, METEOR, ROUGE-L)
  • Diversity (DISTINCT-1/2, Unique N-grams)
  • Semantic Entailment and Consistency (e.g., G-Eval, FEQA, QuestEval)
  • Personality and Act Accuracy (P-ACC, D-ACC)
• Human Evaluations:
  • Criteria include Personality Relevance, Bargaining Efficacy, Outcome Fairness, Coherence, Fluency, and Engagingness, rated typically on 1–4 scales.

Comparative studies demonstrate that:

• Pre-trained LLMs, without personality or argumentation fine-tuning, perform poorly on both response rationality and personalization.
• Fine-tuned models on PACT with RAG and multi-task signals (“FT-Ours”) show marked improvements—up to +27.2% (P-ACC), +17.6% (D-ACC), and significant increases in fluency, fairness, and engagement according to both metrics and human raters (Priya et al., 14 Sep 2025).
• Fine-tuned models also exhibit diminished factual hallucination and improved grounding, attributed to explicit RAG and package data grounding.

6. Implications, Limitations, and Research Trajectory

PAN-DG marks a significant advance in negotiation dialogue generation by merging argumentation rationality with deep personalization:

• Enhanced Realism and Adaptivity: By systematically encoding argumentation and personality, PAN-DG systems can dynamically tailor negotiation tactics, linguistic style, and offer strategies to a counterpart’s profile, closely mimicking human negotiation diversity.
• Methodological Extendability: The PACT dataset and associated training/evaluation pipeline provide a reusable blueprint for PAN-DG in other domains (e.g., job interviews, customer support, sales), supporting extensions to more complex multi-party or multi-issue negotiations.
• Grounding and Hallucination: Integration of background knowledge and explicit personality/argument signals curtails hallucination and off-topic digressions, a persistent issue in unconstrained open-domain dialogue models.

Current limitations and future directions include:

• Profile Complexity: Personality profiles may need further refinement or dimensional enrichment (beyond argumentation, preference, and buying style) for cross-domain portability.
• Long-Range Consistency: Ensuring persona consistency over extended, multi-session negotiations remains challenging, particularly as profiles evolve or new information emerges.
• Evaluation Robustness: Developing standardized, negotiation-specific metrics for outcome fairness, persuasive success, and user satisfaction is a key open area.

7. Broader Context within Negotiation Dialogue Systems

PAN-DG situates itself as an overview of prior research on personalized dialogue (Zheng et al., 2019, Zheng et al., 2019), argumentation-based planning, and goal-driven negotiation systems. While earlier systems focused either on argument logic or surface-level persona, PAN-DG integrates the two, enabling both fine-grained control over linguistic expression and rational plan generation.

By offering a rigorous framework, a rich annotated dataset (PACT), and compelling evaluation evidence, PAN-DG establishes a new paradigm for the development of negotiation dialogue agents capable of both persuasive argumentation and adaptive, persona-consistent communication—a critical step toward robust, human-like negotiation systems in applied AI.