  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 propose Branch-Solve-Merge
(BSM), a Large Language Model program (Schlag et al., 2023) for tackling such
challenging natural language tasks. It consists of branch, solve, and merge
modules 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%.

Computation and Language;Artificial Intelligence;Machine Learning