  Prompting large language models has enabled significant recent progress in
multi-step reasoning over text. However, when applied to text generation from
semi-structured data (e.g., graphs or tables), these methods typically suffer
from low semantic coverage, hallucination, and logical inconsistency. We
propose MURMUR, a neuro-symbolic modular approach to text generation from
semi-structured data with multi-step reasoning. MURMUR is a best-first search
method that generates reasoning paths using: (1) neural and symbolic modules
with specific linguistic and logical skills, (2) a grammar whose production
rules define valid compositions of modules, and (3) value functions that assess
the quality of each reasoning step. We conduct experiments on two diverse
data-to-text generation tasks like WebNLG and LogicNLG. These tasks differ in
their data representations (graphs and tables) and span multiple linguistic and
logical skills. MURMUR obtains significant improvements over recent few-shot
baselines like direct prompting and chain-of-thought prompting, while also
achieving comparable performance to fine-tuned GPT-2 on out-of-domain data.
Moreover, human evaluation shows that MURMUR generates highly faithful and
correct reasoning paths that lead to 26% more logically consistent summaries on
LogicNLG, compared to direct prompting.
