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

• URL: http://arxiv.org/abs/2412.06229v1
• Date: Mon, 09 Dec 2024 06:03:48 GMT
• Title: LLMs as Debate Partners: Utilizing Genetic Algorithms and Adversarial Search for Adaptive Arguments
• Authors: Prakash Aryan,
• Abstract summary: DebateBrawl is an AI-powered debate platform that integrates Large Language Models (LLMs), Genetic Algorithms (GA), and Adversarial Search (AS)<n>The system demonstrates remarkable performance in generating coherent, contextually relevant arguments while adapting its strategy in real-time.<n>The system's ability to maintain high factual accuracy (92% compared to 78% in human-only debates) addresses critical concerns in AI-assisted discourse.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• Adapting University Policies for Generative AI: Opportunities, Challenges, and Policy Solutions in Higher Education [1.2691047660244332]
  The rapid proliferation of generative artificial intelligence (AI) tools has ushered in a transformative era in higher education.<n>This article critically examines the opportunities offered by generative AI, explores the multifaceted challenges it poses, and outlines robust policy solutions.
  arXiv  Detail & Related papers  (2025-06-27T13:49:02Z)
• Avoiding Obfuscation with Prover-Estimator Debate [33.14645106993676]
  We propose a protocol for AI debate that guarantees the correctness of human judgements for complex problems.<n>We show that a dishonest debater can use a computationally efficient strategy that forces an honest opponent to solve a computationally intractable problem to win.
  arXiv  Detail & Related papers  (2025-06-16T15:37:33Z)
• Persuasion and Safety in the Era of Generative AI [0.0]
  The EU AI Act prohibits AI systems that use manipulative or deceptive techniques to undermine informed decision-making.<n>My dissertation addresses the lack of empirical studies in this area by developing a taxonomy of persuasive techniques.<n>It provides resources to mitigate the risks of persuasive AI and fosters discussions on ethical persuasion in the age of generative AI.
  arXiv  Detail & Related papers  (2025-05-18T06:04:46Z)
• Must Read: A Systematic Survey of Computational Persuasion [60.83151988635103]
  AI-driven persuasion can be leveraged for beneficial applications, but also poses threats through manipulation and unethical influence.<n>Our survey outlines future research directions to enhance the safety, fairness, and effectiveness of AI-powered persuasion.
  arXiv  Detail & Related papers  (2025-05-12T17:26:31Z)
• Why Reasoning Matters? A Survey of Advancements in Multimodal Reasoning (v1) [66.51642638034822]
  Reasoning is central to human intelligence, enabling structured problem-solving across diverse tasks. Recent advances in large language models (LLMs) have greatly enhanced their reasoning abilities in arithmetic, commonsense, and symbolic domains. This paper offers a concise yet insightful overview of reasoning techniques in both textual and multimodal LLMs.
  arXiv  Detail & Related papers  (2025-04-04T04:04:56Z)
• How Performance Pressure Influences AI-Assisted Decision Making [57.53469908423318]
  We show how pressure and explainable AI (XAI) techniques interact with AI advice-taking behavior. Our results show complex interaction effects, with different combinations of pressure and XAI techniques either improving or worsening AI advice taking behavior.
  arXiv  Detail & Related papers  (2024-10-21T22:39:52Z)
• Do great minds think alike? Investigating Human-AI Complementarity in Question Answering with CAIMIRA [43.116608441891096]
  Humans outperform AI systems in knowledge-grounded abductive and conceptual reasoning. State-of-the-art LLMs like GPT-4 and LLaMA show superior performance on targeted information retrieval.
  arXiv  Detail & Related papers  (2024-10-09T03:53:26Z)
• OlympicArena: Benchmarking Multi-discipline Cognitive Reasoning for Superintelligent AI [73.75520820608232]
  We introduce OlympicArena, which includes 11,163 bilingual problems across both text-only and interleaved text-image modalities. These challenges encompass a wide range of disciplines spanning seven fields and 62 international Olympic competitions, rigorously examined for data leakage. Our evaluations reveal that even advanced models like GPT-4o only achieve a 39.97% overall accuracy, illustrating current AI limitations in complex reasoning and multimodal integration.
  arXiv  Detail & Related papers  (2024-06-18T16:20:53Z)
• Counterfactual Reasoning Using Predicted Latent Personality Dimensions for Optimizing Persuasion Outcome [13.731895847081953]
  We present a novel approach that tracks a user's latent personality dimensions (LPDs) during ongoing persuasion conversation. We generate tailored counterfactual utterances based on these LPDs to optimize the overall persuasion outcome.
  arXiv  Detail & Related papers  (2024-04-21T23:03:47Z)
• A Unifying Framework for Learning Argumentation Semantics [50.69905074548764]
  We present a novel framework, which uses an Inductive Logic Programming approach to learn the acceptability semantics for several abstract and structured argumentation frameworks in an interpretable way. Our framework outperforms existing argumentation solvers, thus opening up new future research directions in the area of formal argumentation and human-machine dialogues.
  arXiv  Detail & Related papers  (2023-10-18T20:18:05Z)
• ChatEval: Towards Better LLM-based Evaluators through Multi-Agent Debate [57.71597869337909]
  We build a multi-agent referee team called ChatEval to autonomously discuss and evaluate the quality of generated responses from different models. Our analysis shows that ChatEval transcends mere textual scoring, offering a human-mimicking evaluation process for reliable assessments.
  arXiv  Detail & Related papers  (2023-08-14T15:13:04Z)
• The Language Labyrinth: Constructive Critique on the Terminology Used in the AI Discourse [0.0]
  This paper claims, that AI debates are still characterised by a lack of critical distance to metaphors like 'training', 'learning' or 'deciding' As consequence, reflections regarding responsibility or potential use-cases are greatly distorted. It is a conceptual work at the intersection of critical computer science and philosophy of language.
  arXiv  Detail & Related papers  (2023-07-18T14:32:21Z)
• Learning to Prompt in the Classroom to Understand AI Limits: A pilot study [35.06607166918901]
  Large Language Models (LLM) and the derived chatbots, like ChatGPT, have highly improved the natural language processing capabilities of AI systems. However, excitement has led to negative sentiments, even as AI methods demonstrate remarkable contributions. A pilot educational intervention was performed in a high school with 21 students.
  arXiv  Detail & Related papers  (2023-07-04T07:51:37Z)
• Solving NLP Problems through Human-System Collaboration: A Discussion-based Approach [98.13835740351932]
  This research aims to create a dataset and computational framework for systems that discuss and refine their predictions through dialogue. We show that the proposed system can have beneficial discussions with humans improving the accuracy by up to 25 points in the natural language inference task.
  arXiv  Detail & Related papers  (2023-05-19T16:24:50Z)
• The Role of AI in Drug Discovery: Challenges, Opportunities, and Strategies [97.5153823429076]
  The benefits, challenges and drawbacks of AI in this field are reviewed. The use of data augmentation, explainable AI, and the integration of AI with traditional experimental methods are also discussed.
  arXiv  Detail & Related papers  (2022-12-08T23:23:39Z)
• Blessing from Human-AI Interaction: Super Reinforcement Learning in Confounded Environments [19.944163846660498]
  We introduce the paradigm of super reinforcement learning that takes advantage of Human-AI interaction for data driven sequential decision making. In the decision process with unmeasured confounding, the actions taken by past agents can offer valuable insights into undisclosed information. We develop several super-policy learning algorithms and systematically study their theoretical properties.
  arXiv  Detail & Related papers  (2022-09-29T16:03:07Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.