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.

翻译：后编辑已被证明能有效提升大语言模型（如GPT-3.5或GPT-4）生成文本的质量，尤其在直接更新其参数以改进文本质量不可行或成本过高的情况下。然而，仅依赖较小语言模型进行后编辑会限制大语言模型的跨域泛化能力。此外，这些方法中的编辑策略并非针对文本生成任务优化设计。为解决这些局限，我们提出一种神经程序员-解释器方法，在编辑大语言模型输出时保留其域泛化能力。该框架中的编辑动作专为文本生成设计。大量实验表明，程序员-解释器显著提升了GPT-3.5在逻辑形式到文本转换及低资源机器翻译中的表现，在跨域场景中超越其他最先进的（SOTA）大语言模型后编辑方法。