Summary of Llms As Debate Partners: Utilizing Genetic Algorithms and Adversarial Search For Adaptive Arguments, by Prakash Aryan
LLMs as Debate Partners: Utilizing Genetic Algorithms and Adversarial Search for Adaptive Arguments
by Prakash Aryan
First submitted to arxiv on: 9 Dec 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL); Computers and Society (cs.CY); Neural and Evolutionary Computing (cs.NE)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The DebateBrawl platform combines Large Language Models (LLMs), Genetic Algorithms (GA), and Adversarial Search (AS) to create an adaptive debating experience. This innovative AI-powered debate tool addresses limitations of traditional LLMs by incorporating evolutionary optimization and game-theoretic techniques, generating coherent arguments while adapting its strategy in real-time. Experimental results show balanced outcomes between AI and human participants, with the AI system achieving a competitive average score. User feedback indicates significant improvements in debating skills and a satisfactory learning experience. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary DebateBrawl is an AI-powered debate platform that makes it easier to learn how to argue effectively. It combines artificial intelligence and game theory to create a tool that can have debates with humans, and even helps people improve their own debating skills. The system does well in debates, and most users find the experience helpful. DebateBrawl also focuses on making sure its arguments are accurate and diverse. |
Keywords
» Artificial intelligence » Optimization
