Humans can reason compositionally when presented with new tasks. Previous research shows that appropriate prompting techniques enable large language models (LLMs) to solve artificial compositional generalization tasks such as SCAN. In this work, we identify additional challenges in more realistic semantic parsing tasks with larger vocabulary and refine these prompting techniques to address them. Our best method is based on least-to-most prompting: it decomposes the problem using prompting-based syntactic parsing, then uses this decomposition to select appropriate exemplars and to sequentially generate the semantic parse. This method allows us to set a new state of the art for CFQ while requiring only 1% of the training data used by traditional approaches. Due to the general nature of our approach, we expect similar efforts will lead to new results in other tasks and domains, especially for knowledge-intensive applications.

通过逐步提示的基于句法分析的分解，本研究发现了更多语义分析任务中的挑战。这一最优方法使我们在CFQ上取得了最新的技术成果，同时只需要传统方法所需训练数据的1％。由于我们方法的普适性，我们期望类似的方法将在知识密集型应用中取得新的成果。

大型语言模型的组合语义解析