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

arXiv:2408.14575 · cs.AI · Submitted 2024-08-26 · Read on arXiv

cs.AI

Submitted: 2024-08-26

Updated: 2026-09-14

Comments: Version 5, September 2026. Versions 1-4 presented the entropy-duality principle as a theorem of optimal pairing; Section 2.4 and Appendix C state what the underlying inequalities establish and what they do not, and the empirical claims are rescoped accordingly. The two case studies are unchanged

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Accuracy — 25× in this paper · explained in 1 episode
• Mutual Information — 24× in this paper
• Wasserstein Distance — 16× in this paper · explained in 4 episodes
• Cross Entropy — 10× in this paper
• Robustness — 7× in this paper · explained in 5 episodes
• Jensen-Shannon Divergence — 5× in this paper
• Multi-Agent Debate — 5× in this paper
• Alignment — 4× in this paper · explained in 1 episode
• Entropy Duality Theorem — 4× in this paper
• Kullback-Leibler Divergence — 4× in this paper
• Hallucination — 3× in this paper · explained in 3 episodes
• RAG — 3× in this paper · explained in 3 episodes
• Redundancy — 3× in this paper · explained in 2 episodes
• Accountability — 2× in this paper · explained in 2 episodes
• Adaptability — 2× in this paper · explained in 1 episode
• Aggregation — 2× in this paper · explained in 1 episode
• Consistency — 2× in this paper · explained in 3 episodes
• Cooperation — 2× in this paper · explained in 2 episodes
• Core — 2× in this paper · explained in 2 episodes
• In-Context Learning — 2× in this paper · explained in 4 episodes
• Benchmark — 1× in this paper · explained in 2 episodes
• Calibration — 1× in this paper · explained in 7 episodes
• Commentary — 1× in this paper · explained in 2 episodes
• Deep Reinforcement Learning — 1× in this paper · explained in 2 episodes
• Diagnostic Tools — 1× in this paper · explained in 2 episodes
• Distillation — 1× in this paper · explained in 2 episodes
• Explainability — 1× in this paper · explained in 3 episodes
• Generalization — 1× in this paper · explained in 13 episodes
• Knowledge Distillation — 1× in this paper · explained in 11 episodes
• Kullback-Leibler (KL) Divergence — 1× in this paper · explained in 3 episodes
• Large Language Models (LLMs) — 1× in this paper · explained in 6 episodes
• Multi-Agent Framework — 1× in this paper · explained in 3 episodes
• Multi-agent Collaboration — 1× in this paper · explained in 2 episodes
• Optimal Transport — 1× in this paper · explained in 5 episodes
• Reinforcement Learning — 1× in this paper · explained in 4 episodes
• Retrieval-Augmented Generation — 1× in this paper
• Selection Bias — 1× in this paper · explained in 4 episodes

Sources

• Prompting Large Language Models With the Socratic Method
• GPT-4 Technical Report
• Can LLMs Beat Humans in Debating? A Dynamic Multi-agent Framework for Competitive Debate

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