arxiv preprint – Branch-Solve-Merge Improves Large Language Model Evaluation and Generation

In this episode, we discuss Branch-Solve-Merge Improves Large Language Model Evaluation and Generation by Swarnadeep Saha, Omer Levy, Asli Celikyilmaz, Mohit Bansal, Jason Weston, Xian Li. The paper introduces a program called BRANCH-SOLVE-MERGE (BSM) designed to enhance the performance of Large Language Models (LLMs) on complex natural language tasks. BSM uses a three-module approach that breaks tasks into parallel sub-tasks, solves each independently, and then integrates the results. The implementation of BSM shows significant improvements in LLM tasks such as response evaluation and constrained text generation, increasing human-LLM agreement, reducing biases, and enhancing story coherence and constraint satisfaction.