Improving Retrieval-augmented Text-to-SQL with AST-based Ranking and Schema Pruning

We focus on Text-to-SQL semantic parsing from the perspective of Large Language Models. Motivated by challenges related to the size of commercial database schemata and the deployability of business intelligence solutions, we propose an approach that dynamically retrieves input database information and uses abstract syntax trees to select few-shot examples for in-context learning. Furthermore, we investigate the extent to which an in-parallel semantic parser can be leveraged for generating $\textit{approximated}$ versions of the expected SQL queries, to support our retrieval. We take this approach to the extreme--we adapt a model consisting of less than $500$M parameters, to act as an extremely efficient approximator, enhancing it with the ability to process schemata in a parallelised manner. We apply our approach to monolingual and cross-lingual benchmarks for semantic parsing, showing improvements over state-of-the-art baselines. Comprehensive experiments highlight the contribution of modules involved in this retrieval-augmented generation setting, revealing interesting directions for future work.

The Fire Thief Is Also the Keeper: Balancing Usability and Privacy in Prompts

The rapid adoption of online chatbots represents a significant advancement in artificial intelligence. However, this convenience brings considerable privacy concerns, as prompts can inadvertently contain sensitive information exposed to large language models (LLMs). Limited by high computational costs, reduced task usability, and excessive system modifications, previous works based on local deployment, embedding perturbation, and homomorphic encryption are inapplicable to online prompt-based LLM applications. To address these issues, this paper introduces Prompt Privacy Sanitizer (i.e., ProSan), an end-to-end prompt privacy protection framework that can produce anonymized prompts with contextual privacy removed while maintaining task usability and human readability. It can also be seamlessly integrated into the online LLM service pipeline. To achieve high usability and dynamic anonymity, ProSan flexibly adjusts its protection targets and strength based on the importance of the words and the privacy leakage risk of the prompts. Additionally, ProSan is capable of adapting to diverse computational resource conditions, ensuring privacy protection even for mobile devices with limited computing power. Our experiments demonstrate that ProSan effectively removes private information across various tasks, including question answering, text summarization, and code generation, with minimal reduction in task performance.