  Post-editing has proven effective in improving the quality of text generated
by large language models (LLMs) such as GPT-3.5 or GPT-4, particularly when
direct updating of their parameters to enhance text quality is infeasible or
expensive. However, relying solely on smaller language models for post-editing
can limit the LLMs' ability to generalize across domains. Moreover, the editing
strategies in these methods are not optimally designed for text-generation
tasks. To address these limitations, we propose a neural programmer-interpreter
approach that preserves the domain generalization ability of LLMs when editing
their output. The editing actions in this framework are specifically devised
for text generation. Extensive experiments demonstrate that the
programmer-interpreter significantly enhances GPT-3.5's performance in logical
form-to-text conversion and low-resource machine translation, surpassing other
state-of-the-art (SOTA) LLM post-editing methods in cross-domain settings.
