Large Language Models (LLMs)  are frequently used for multi-faceted language generation and evaluation tasks that involve satisfying intricate user constraints or
taking into account multiple aspects and criteria. However, their performance can fall short, due to the model’s lack of coherence and inability to plan and decompose the problem.
We proposeBranch-Solve-Merge(BSM) , a Large Language Model programfor tackling such challenging natural language tasks. It consists ofbranch,solve, andmergemodules that are parameterized with specific prompts to the base LLM.
These three modules plan a decomposition of the task into multiple parallel sub-tasks, independently solve them, and fuse the solutions to the sub-tasks. We apply our method to the tasks of LLM response evaluation and constrained text generation and evaluate its effectiveness with multiple LLMs, including Vicuna, LLaMA-2-chat, and GPT-4. BSM improves the evaluation correctness and consistency
for each LLM by enhancing human-LLM agreement by up to 26%, reducing length and pairwise position biases by up to 50%, and allowing LLaMA-2-chat to match or outperform GPT-4 on most domains. On the constraint story generation task, BSM improves the coherence of the stories while also improving constraint satisfaction by 12%.