Automated Proving of Shannon-Type Entropy Inequalities via Fine-Tuned Language Models and Guided Tree Search
Researchers have developed a method to automate the proving of Shannon-type entropy inequalities using fine-tuned language models and guided tree search. Their small-scale models, with parameters ranging from 0.6B to 1.7B, achieved an 85% success rate on a test set of 60 inequalities involving 10 to 15 variables. This approach significantly outperformed zero-shot prompting with larger models like GPT-5.5 and Psitip. An ablation study indicated that a 4096-token training context length and a non-skewed data distribution were optimal for performance. AI
IMPACT Demonstrates a novel application of fine-tuned LLMs for complex mathematical proofs, potentially accelerating research in information theory.