Personality-Aware Reinforcement Learning for Persuasive Dialogue with LLM-Driven Simulation
Introduces personality-aware RL with D3QN and P4G dataset to improve persuasive dialogue outcomes.
Key Findings
Methodology
The approach integrates three modules: strategy-oriented interaction using MMR retrieval; personality-aware user representation with 81-dimensional embeddings; D3QN model optimized with composite rewards.
Key Results
- Result 1: Personality conditioning improves cumulative persuasion rewards by ~15%.
- Result 2: LLM-driven simulation enhances generalization to unseen behaviors, boosting accuracy by ~10%.
- Result 3: Change-of-mind penalty reduces retractions and slightly improves donation rates (~5%).
Significance
This study addresses dynamic personality modeling in persuasive dialogue, significantly improving adaptability and effectiveness, with implications for academia and industry.
Technical Contribution
Proposes a novel framework combining dynamic personality estimation and RL, overcoming limitations of static models and optimizing strategy selection with D3QN.
Novelty
First to integrate dynamic personality embeddings with RL for persuasive dialogue, distinct from prior static modeling approaches.
Limitations
- Limitation 1: Personality estimation depends on LLM-generated dialogue quality, which may introduce bias.
- Limitation 2: Composite reward weights are empirically set, lacking theoretical justification.
- Limitation 3: Experiments rely heavily on P4G dataset, limiting generalizability.
Future Work
Future research could explore more sophisticated personality modeling methods and validate applicability in broader scenarios.
AI Executive Summary
Persuasive dialogue systems aim to influence user decisions through multi-turn interactions, but existing methods struggle to adapt to dynamic psychological changes.
This paper introduces a personality-aware reinforcement learning framework combining D3QN and LLM-driven simulation to dynamically model user personality and optimize strategies. Using the P4G dataset and simulated dialogues, the framework significantly improves persuasion outcomes.
Experiments show personality embeddings enhance cumulative rewards, LLM simulation boosts generalization, and penalties reduce agreement retractions. This approach offers new insights for designing adaptive dialogue systems and highlights future research directions.
Deep Analysis
Background
Persuasive dialogue systems have gained attention in fields like fundraising and health promotion. Traditional methods rely on static personality modeling, failing to capture dynamic psychological changes.
Core Problem
The core challenge is dynamically modeling user personality and optimizing persuasive strategies, as existing methods struggle with behavioral complexity and diversity.
Innovation
Key innovations include: dynamic personality embeddings integrated with RL; LLM-driven simulation for diverse data generation; composite reward design balancing short-term and long-term goals.
Methodology
- �� Strategy-oriented interaction: Generates diverse responses using MMR retrieval.
- �� Personality modeling: Creates 81-dimensional embeddings for dynamic user representation.
- �� D3QN model: Optimizes strategy selection using composite rewards.
Experiments
Experiments use the P4G dataset and LLM-simulated dialogues to evaluate the impact of personality embeddings on reward improvement.
Results
Personality embeddings improve cumulative rewards by 15%, LLM simulation enhances generalization by 10%, and penalties reduce agreement retractions.
Applications
Applicable to fundraising and health promotion, helping optimize persuasive strategies.
Limitations & Outlook
Personality estimation depends on LLM quality, reward weights lack theoretical basis, and experiments focus on P4G dataset.
Plain Language Accessible to non-experts
Imagine a chef tailoring a menu based on customer preferences. The system acts like the chef, learning user tastes through dialogue and optimizing recommendations.
ELI14 Explained like you're 14
Think of playing a game with friends where you adjust your strategy based on their personality, like encouraging or challenging them. This system does the same, trying to influence decisions through conversation!
Glossary
Reinforcement Learning
A machine learning method optimizing strategies via reward signals.
Used to optimize persuasive strategy selection.
Personality Embedding
Transforms user personality traits into numerical vectors.
Used for dynamic user modeling.
LLM Simulation
Generates dialogue data using large language models.
Expands training data and enhances generalization.
Dueling Double DQN
An RL algorithm combining value and advantage streams.
Optimizes persuasive strategy selection.
Composite Reward
An optimization target combining multiple reward signals.
Balances short-term and long-term persuasion outcomes.
Open Questions Unanswered questions from this research
- 1 How to mitigate LLM generation bias affecting personality estimation?
- 2 What theoretical methods can optimize composite reward weights?
Applications
Immediate Applications
Charitable Fundraising
Optimizes persuasive strategies to increase donation rates.
Health Promotion
Influences user decisions on health through dialogue.
Long-term Vision
Intelligent Dialogue Assistants
Develop adaptive systems for education, marketing, and beyond.
Abstract
Effective persuasive dialogue agents adapt their strategies to individual users, accounting for the evolution of their psychological states and intentions throughout conversations. We present a personality-aware reinforcement learning approach comprising three main modules: (1) a Strategy-Oriented Interaction Framework, which serves as an agenda-based strategy controller that selects strategy-level actions and generate responses via Maximal Marginal Relevance (MMR) retrieval to ensure contextual relevance, diversity, and scalable data generation; (2) Personality-Aware User Representation Learning, which produces an 81-dimensional mixed-type embedding predicted at each turn from recent exchanges and appended to the reinforcement learning state; and (3) a Dueling Double DQN (D3QN) model and Reward Prediction, in which the policy is conditioned on dialogue history and turn-level personality estimates and trained using a composite reward incorporating agreement intent, donation amount, and changeof-mind penalties. We use an agenda-based LLM simulation pipeline to generate diverse interactions, from which personality estimation is inferred from the generated utterances. Experiments on the PersuasionForGood (P4G) dataset augmented with simulated dialogues reveal three main findings: (i) turn-level personality conditioning improves policy adaptability and cumulative persuasion rewards; (ii) LLM-driven simulation enhances generalization to unseen user behaviors; and (iii) incorporating a change-of-mind penalty reduces post-agreement retractions while slightly improving donation outcomes. These results demonstrate that structured interaction, dynamic personality estimation, and behaviorally informed rewards together yield more effective persuasive policies.