EVINCE: Optimizing Multi-LLM Dialogues Using Conditional Statistics and Information Theory

• URL: http://arxiv.org/abs/2408.14575v4
• Date: Wed, 29 Jan 2025 20:48:59 GMT
• Title: EVINCE: Optimizing Multi-LLM Dialogues Using Conditional Statistics and Information Theory
• Authors: Edward Y. Chang,
• Abstract summary: EVINCE is a novel framework for optimizing multi-LLM dialogues.<n>It addresses limitations in multi-agent debate (MAS) frameworks.<n>$EVINCE$ emerges as a structured and highly effective framework for multi-LLM collaboration.
• Score: 2.5200794639628032
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: EVINCE (Entropy and Variation IN Conditional Exchanges) is a novel framework for optimizing multi-LLM dialogues using conditional statistics and information theory. It addresses limitations in multi-agent debate (MAS) frameworks, where multiple LLMs ``chat'' without behavior modulation or mutual information quality assessment. Using dual entropy optimization to balance perspective diversity and prior knowledge, $\EVINCE$ provides quantitative tools to dynamically regulate LLM linguistic behaviors. When mutual information is low and both cross-entropy and Wasserstein distance are high, EVINCE promotes contentious dialogues to expose diverse perspectives and uncover inconsistencies. Conversely, as cross-entropy decreases and mutual information stabilizes, it transitions discussions into a conciliatory phase, encouraging compromise and acknowledgment of valid points. Using information-theoretic metrics and optimizing mutual information, $\EVINCE$ emerges as a structured and highly effective framework for multi-LLM collaboration.

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