Text-to-SQL aims to convert natural language questions into executable SQL queries.While previous approaches, such as skeleton-masked selection, have demonstrated strong performance by retrieving similar training examples to guide large language models (LLMs), they struggle in real-world scenarios where such examples are unavailable.To overcome this limitation, we propose Self-Augmentation incontext learning with Fine-grained Example selection for Text-to-SQL (SAFE-SQL), a novel unsupervised framework that enhances SQL generation by generating and intelligently filtering self-augmented examples.SAFE-SQL leverages an LLM to generate diverse Textto-SQL examples, which are then filtered by a novel fine-grained mechanism using criteria for semantic similarity, structural alignment, and reasoning path quality to curate highquality in-context learning examples.Leveraging these carefully selected self-generated examples, SAFE-SQL significantly surpasses previous zero-shot and few-shot Text-to-SQL frameworks, achieving superior execution accuracy.Notably, our approach demonstrates substantial performance gains in challenging extra hard and unseen scenarios, where conventional methods often struggle.

Through the advent of pre-trained language models, there have been notable advancements in abstractive summarization systems.Simultaneously, a considerable number of novel methods for evaluating factual consistency in abstractive summarization systems has been developed.But these evaluation approaches incorporate substantial limitations, especially on refinement and interpretability.In this work, we propose highly effective and interpretable factual inconsistency detection method FIZZ (Factual Inconsistency Detection by Zoom-in Summary and Zoom-out Document) for abstractive summarization systems that is based on fine-grained atomic facts decomposition.Moreover, we align atomic facts decomposed from the summary with the source document through adaptive granularity expansion.These atomic facts represent a more fine-grained unit of information, facilitating detailed understanding and interpretability of the summary's factual inconsistency.Experimental results demonstrate that our proposed factual consistency checking system significantly outperforms existing systems.We release the code at https://github.com/plm3332/FIZZ.