PersuaRL: Reinforcement Learning-Driven Multi-Expert Selection for Persuasive Dialogue Generation in Insurance
PersuaRL leverages RL-driven multi-expert selection to significantly enhance persuasive dialogue generation in insurance.
Key Findings
Methodology
PersuaRL integrates a lightweight selector, specialized expert modules, and a generator. Using GRPO, the selector dynamically activates experts, while the generator synthesizes contextually persuasive responses. Alternating optimization ensures co-adaptation.
Key Results
- Result 1: On InsureDial, PersuaRL achieved ~12% BLEU-2 and ~10% METEOR improvement over baselines.
- Result 2: Strong cross-domain generalization was observed in tourism datasets, maintaining superior persuasion performance.
- Result 3: Ablation studies revealed strategy alignment and contextual relevance rewards as key contributors to performance gains.
Significance
This work addresses the lack of persuasive capabilities in insurance dialogue systems, offering a novel RL-based framework that improves context understanding and strategy adaptability, with implications for customer service and sales.
Technical Contribution
Introduced an RL-based multi-expert framework, modeling expert selection as a context-conditioned decision problem. Alternating optimization enables selector-generator co-adaptation, a departure from static prompting methods.
Novelty
PersuaRL is the first framework to dynamically select experts via RL for persuasive dialogue. It surpasses static or monolithic generation approaches by enhancing flexibility and contextual adaptability.
Limitations
- Limitation 1: Reward function design heavily relies on domain knowledge, limiting cross-domain scalability.
- Limitation 2: High computational cost makes it challenging for resource-constrained environments.
- Limitation 3: Selector struggles to maintain consistent strategy in extremely long dialogues.
Future Work
Future work could explore more efficient selector training, generalizable reward functions, and expansion to domains like healthcare or education.
AI Executive Summary
In the insurance domain, customer service dialogues must not only convey information but also persuade users to make informed decisions. Existing LLMs often lack the flexibility and contextual sensitivity required for such tasks.
PersuaRL introduces an RL-driven multi-expert selection framework that dynamically coordinates expert modules (e.g., intent recognition, sentiment analysis) to generate contextually persuasive responses. Its core innovation lies in modeling expert selection as a context-conditioned decision problem and employing alternating optimization for selector-generator co-adaptation.
Experimental results demonstrate that PersuaRL significantly outperforms baselines on the newly introduced InsureDial dataset, achieving strong cross-domain generalization. While computational cost and domain dependency remain challenges, this framework lays a robust foundation for building smarter dialogue systems with enhanced persuasion capabilities.
Deep Analysis
Background
Recent advances in LLMs have revolutionized conversational AI, enabling applications in customer service and sales. However, these models struggle with multi-turn, context-sensitive persuasive dialogues, especially in domains like insurance where trust and clarity are critical.
Core Problem
Insurance dialogues require not only information delivery but also persuasive guidance to help users make decisions. Current models lack the ability to adapt strategies dynamically, limiting their effectiveness in real-world scenarios.
Innovation
Key innovations of PersuaRL include:
- �� Introduction of InsureDial, a persuasive insurance dialogue dataset.
- �� RL-based multi-expert selection framework, modeling expert selection as a context-conditioned decision problem.
- �� Composite reward function optimizing strategy alignment, intent consistency, and contextual relevance.
Methodology
- �� Selector: Uses GRPO to dynamically select expert modules per turn.
- �� Experts: Specialized modules for intent recognition, sentiment analysis, etc.
- �� Generator: Integrates expert outputs to produce final responses.
- �� Alternating Optimization: Selector and generator are trained iteratively to improve overall performance.
Experiments
Experiments used the InsureDial dataset with 1,931 dialogues. Baselines included LLAMA and QWEN models. Metrics like BLEU-2 and METEOR were used, alongside cross-domain tests and ablation studies.
Results
PersuaRL outperformed baselines on InsureDial, achieving ~12% BLEU-2 improvement. Cross-domain tests confirmed strong generalization. Ablation studies validated the reward function's effectiveness.
Applications
The framework is applicable to insurance sales, customer support, and other domains requiring persuasive dialogue systems to improve user experience and conversion rates.
Limitations & Outlook
The model's reliance on domain-specific knowledge limits scalability. High computational costs pose challenges for deployment in resource-constrained settings.
Plain Language Accessible to non-experts
Imagine buying car insurance. Instead of a generic chatbot, you get a super-smart assistant that understands your needs and emotions. PersuaRL acts like this assistant, dynamically picking the right 'experts' (like intent or sentiment analyzers) to craft responses that are both helpful and persuasive.
ELI14 Explained like you're 14
Think about asking a chatbot, 'Does this insurance cover roadside assistance?' PersuaRL is like a super-smart bot that not only answers but also convinces you why their plan is perfect for you. It's like having a really persuasive friend who knows everything about insurance!
Glossary
Reinforcement Learning
A machine learning approach where models learn by receiving rewards or penalties for actions.
Used to train the selector to choose the best expert modules dynamically.
Multi-Expert System
A system composed of specialized modules, each focusing on a specific task.
PersuaRL's experts handle tasks like intent recognition and sentiment analysis.
InsureDial
A dataset focused on persuasive motor insurance dialogues with 1,931 conversations.
Used to train and evaluate the PersuaRL framework.
GRPO
Group Relative Policy Optimization, an RL algorithm for optimizing expert selection policies.
Drives the selector's decision-making in PersuaRL.
Reward Function
A mechanism to evaluate the quality of generated responses based on multiple criteria.
Guides the selector to optimize strategy alignment and contextual relevance.
Open Questions Unanswered questions from this research
- 1 How can reward functions be designed to generalize across domains?
- 2 What methods can reduce computational costs for real-world deployment?
Applications
Immediate Applications
Insurance Sales
Helps insurance companies improve conversion rates through intelligent dialogue systems.
Customer Support
Provides personalized, persuasive assistance for resolving customer queries effectively.
Long-term Vision
Cross-Domain Dialogue Systems
Expanding to healthcare, education, and other domains for broader intelligent services.
Abstract
Large Language Models (LLMs) are revolutionizing digital communication by powering conversational agents deployed across domains such as customer service, digital sales, and insurance. These agents, built on LLMs, can understand user input, retrieve relevant information, and generate coherent responses. However, while they excel at factual communication, they often lack the ability to engage in truly persuasive, context-sensitive dialogue, especially in domains like insurance, where trust and clarity are critical. Building on this need within the insurance domain, our work focuses on improving the persuasiveness of digital agents, aka LLMs. To support this, we introduce InsureDial, a Persuasive Insurance Dialogue dataset, designed to capture the nuances of persuasive communication specific to motor insurance interactions. We introduce PersuaRL, a reinforcement learning-based framework that equips LLM-driven dialogue agents with the ability to adaptively explore, select, and coordinate strategies across multiple expert modules, guided by the evolving dialogue context, to achieve more effective persuasion. We conduct extensive automatic human and qualitative evaluations on two benchmark persuasion dialogue datasets, including our InsureDial. Our evaluations consistently demonstrate that PersuaRL outperforms baseline, generating contextually appropriate and highly persuasive responses.