Paper ID: 2408.14575

EVINCE: Optimizing Adversarial LLM Dialogues via Conditional Statistics and Information Theory

Edward Y. Chang

This paper introduces $\EVINCE$ (Entropy and Variation IN Conditional Exchanges), a dialogue framework advancing Artificial General Intelligence (AGI) by enhancing versatility, adaptivity, and reasoning in large language models (LLMs). Leveraging adversarial debate and a novel dual entropy theory, EVINCE improves prediction accuracy, robustness, and stability in LLMs by integrating statistical modeling, information theory, and machine learning to balance diverse perspective exploration with strong prior exploitation. The framework's effectiveness is demonstrated through consistent convergence of information-theoretic metrics, particularly improved mutual information, fostering productive LLM collaboration. We apply $\EVINCE$ to healthcare, showing improved disease diagnosis, and discuss its broader implications for decision-making across domains. This work provides theoretical foundations and empirical validation for $\EVINCE$, paving the way for advancements in LLM collaboration and AGI development.

Submitted: Aug 26, 2024