EVINCE: Optimizing Adversarial LLM Dialogues via Conditional Statistics and Information Theory
<|reference_start|>EVINCE: Optimizing Adversarial LLM Dialogues via Conditional Statistics and Information Theory: 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.<|reference_end|>
@article{chang2024evince:,
    title={EVINCE: Optimizing Adversarial LLM Dialogues via Conditional Statistics
  and Information Theory},
    author={Edward Y. Chang},
    journal={arXiv preprint arXiv:2408.14575},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.14575},
    primaryClass={cs.AI}
}