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Large Language Models (LLMs) are frequently used for multi-faceted languagegeneration and evaluation tasks that involve satisfying intricate userconstraints or taking into account multiple aspects and criteria. However,their performance can fall short, due to the model's lack of coherence andinability to plan and decompose the problem. We propose Branch-Solve-Merge(BSM), a Large Language Model program (Schlag et al., 2023) for tackling suchchallenging natural language tasks. It consists of branch, solve, and mergemodules that are parameterized with specific prompts to the base LLM. Thesethree modules plan a decomposition of the task into multiple parallelsub-tasks, independently solve them, and fuse the solutions to the sub-tasks.We apply our method to the tasks of LLM response evaluation and constrainedtext generation and evaluate its effectiveness with multiple LLMs, includingVicuna, LLaMA-2-chat, and GPT-4. BSM improves the evaluation correctness andconsistency for each LLM by enhancing human-LLM agreement by up to 26%,reducing length and pairwise position biases by up to 50%, and allowingLLaMA-2-chat to match or outperform GPT-4 on most domains. On the constraintstory generation task, BSM improves the coherence of the stories while alsoimproving constraint satisfaction by 12%.

Branch-Solve-Merge Improves Large Language Model Evaluation and Generation