Entropy-Tree: A Novel Approach to Decoding for LLMs

Large language models (LLMs) have demonstrated remarkable reasoning capabilities, but existing decoding strategies often rely on random exploration or redundant multi-sampling. To address these limitations, Entropy-Tree has been proposed, a tree-based decoding method that uses entropy as a signal for branching decisions.

The key idea is to expand the search tree only at positions where the model exhibits genuine uncertainty. This approach allows for greater accuracy and better calibration in reasoning tasks. Experimental results show that Entropy-Tree outperforms the Multi-chain method in terms of pass@k across multiple models and datasets. Furthermore, the predictive entropy of Entropy-Tree demonstrates better AUROC compared to several traditional metrics.

Implications and Benefits

Entropy-Tree represents a step forward in optimizing decoding processes for LLMs. By unifying efficient structured exploration and reliable uncertainty estimation, this method offers a more effective and precise approach to leveraging the reasoning capabilities of language models. The ability to focus the search only where needed reduces redundancy and improves overall efficiency.