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LLMs as Debate Partners: Utilizing Genetic Algorithms and Adversarial Search for Adaptive Arguments

Prakash Aryan

TL;DR

DebateBrawl introduces an AI debate platform that fuses Large Language Models (LLMs) with Genetic Algorithms (GA) and Adversarial Search (AS) to deliver adaptive, strategy-aware arguments. In 23 debates, the AI achieved an average score of 2.72, closely matching or slightly exceeding human performance (2.67), while delivering high factual accuracy (92%) compared with human-only debates (78%). The system relies on a multi-model LLM interface (LLaMA, Gemma, Phi) and a GA+AS core to evolve strategies and anticipate counterarguments, supported by robust evaluation and secure data management. The results indicate strong potential for AI-assisted debate training and enhanced public discourse, while underscoring ethical considerations, transparency, and safeguards for responsible deployment of persuasive AI systems.

Abstract

This paper introduces DebateBrawl, an innovative AI-powered debate platform that integrates Large Language Models (LLMs), Genetic Algorithms (GA), and Adversarial Search (AS) to create an adaptive and engaging debating experience. DebateBrawl addresses the limitations of traditional LLMs in strategic planning by incorporating evolutionary optimization and game-theoretic techniques. The system demonstrates remarkable performance in generating coherent, contextually relevant arguments while adapting its strategy in real-time. Experimental results involving 23 debates show balanced outcomes between AI and human participants, with the AI system achieving an average score of 2.72 compared to the human average of 2.67 out of 10. User feedback indicates significant improvements in debating skills and a highly satisfactory learning experience, with 85% of users reporting improved debating abilities and 78% finding the AI opponent appropriately challenging. The system's ability to maintain high factual accuracy (92% compared to 78% in human-only debates) while generating diverse arguments addresses critical concerns in AI-assisted discourse. DebateBrawl not only serves as an effective educational tool but also contributes to the broader goal of improving public discourse through AI-assisted argumentation. The paper discusses the ethical implications of AI in persuasive contexts and outlines the measures implemented to ensure responsible development and deployment of the system, including robust fact-checking mechanisms and transparency in decision-making processes.

LLMs as Debate Partners: Utilizing Genetic Algorithms and Adversarial Search for Adaptive Arguments

TL;DR

DebateBrawl introduces an AI debate platform that fuses Large Language Models (LLMs) with Genetic Algorithms (GA) and Adversarial Search (AS) to deliver adaptive, strategy-aware arguments. In 23 debates, the AI achieved an average score of 2.72, closely matching or slightly exceeding human performance (2.67), while delivering high factual accuracy (92%) compared with human-only debates (78%). The system relies on a multi-model LLM interface (LLaMA, Gemma, Phi) and a GA+AS core to evolve strategies and anticipate counterarguments, supported by robust evaluation and secure data management. The results indicate strong potential for AI-assisted debate training and enhanced public discourse, while underscoring ethical considerations, transparency, and safeguards for responsible deployment of persuasive AI systems.

Abstract

This paper introduces DebateBrawl, an innovative AI-powered debate platform that integrates Large Language Models (LLMs), Genetic Algorithms (GA), and Adversarial Search (AS) to create an adaptive and engaging debating experience. DebateBrawl addresses the limitations of traditional LLMs in strategic planning by incorporating evolutionary optimization and game-theoretic techniques. The system demonstrates remarkable performance in generating coherent, contextually relevant arguments while adapting its strategy in real-time. Experimental results involving 23 debates show balanced outcomes between AI and human participants, with the AI system achieving an average score of 2.72 compared to the human average of 2.67 out of 10. User feedback indicates significant improvements in debating skills and a highly satisfactory learning experience, with 85% of users reporting improved debating abilities and 78% finding the AI opponent appropriately challenging. The system's ability to maintain high factual accuracy (92% compared to 78% in human-only debates) while generating diverse arguments addresses critical concerns in AI-assisted discourse. DebateBrawl not only serves as an effective educational tool but also contributes to the broader goal of improving public discourse through AI-assisted argumentation. The paper discusses the ethical implications of AI in persuasive contexts and outlines the measures implemented to ensure responsible development and deployment of the system, including robust fact-checking mechanisms and transparency in decision-making processes.

Paper Structure

This paper contains 34 sections, 13 figures, 3 tables.

Table of Contents

  1. Introduction
  2. Related Works
  3. Evolutionary Algorithms and LLMs
  4. LLMs in Game Design and Creative Tasks
  5. LLMs in Decision-Making and Planning
  6. LLMs in Recommender Systems
  7. LLMs in Neural Architecture Search and Model Optimization
  8. LLMs in Molecular Design and Materials Science
  9. Explainable AI and LLMs in Genetic Programming
  10. LLMs in Artificial Evolutionary Intelligence
  11. Challenges and Ethical Considerations
  12. Methodology
  13. System Architecture
  14. Frontend Architecture
  15. Backend Architecture
  16. ...and 19 more sections

Figures (13)

  • Figure 1: Overall System Architecture of DebateBrawl
  • Figure 2: Frontend Architecture of DebateBrawl
  • Figure 3: Backend Architecture of DebateBrawl
  • Figure 4: LLM Interface and Model Interactions
  • Figure 5: Genetic Algorithm Process Flow
  • ...and 8 more figures