  Large language models (LLMs) have demonstrated impressive capabilities in
natural language generation. However, their output quality can be inconsistent,
posing challenges for generating natural language from logical forms (LFs).
This task requires the generated outputs to embody the exact semantics of LFs,
without missing any LF semantics or creating any hallucinations. In this work,
we tackle this issue by proposing a novel generate-and-rerank approach. Our
approach involves initially generating a set of candidate outputs by prompting
an LLM and subsequently reranking them using a task-specific reranker model. In
addition, we curate a manually collected dataset to evaluate the alignment
between different ranking metrics and human judgements. The chosen ranking
metrics are utilized to enhance the training and evaluation of the reranker
model. By conducting extensive experiments on three diverse datasets, we
demonstrate that the candidates selected by our reranker outperform those
selected by baseline methods in terms of semantic consistency and fluency, as
measured by three comprehensive metrics. Our findings provide strong evidence
for the effectiveness of our approach in improving the quality of generated
outputs.
