LIST : Linearly Incremental SQL Translator for Single-Hop Reasoning, Generation and Verification (2025)

Abstract: SQL languages often feature nested structures that require robust interaction with databases. Aside from the well-validated schema linking methods on PLMs and LLMs, we introduce the Linearly Incremental SQL Translator (LIST), a novel algorithmic toolkit designed to leverage the notable reasoning and tool interaction capabilities inherent in LLMs. LIST transforms complex SQL queries into grammatically verifiable sub-queries which are arranged sequentially to reflect single-hop reasoning steps, enhancing both the granularity and accuracy of database interactions. With in-context learning, our experiments demonstrated significant improvements, achieving notable performance of 60.56% and 56.32% on the BIRD dataset with GPT-4o and Llama-3-70B-Instruct. To the best of our knowledge, this achieves SOTA performance among non-schema linking methods, also surpassing a series of schema linking based approaches at a comparable or better cost.

## introduction the rapid development of large language models has enabled strong generalization and reasoning capabilities @xcite , making it possible to tackle challenging tasks such as production-level text-to-sql. this task, crucial for making database interactions accessible to nontechnical users, has seen significant progress on benchmarks like spider @xcite and bird @xcite . we consider sql generation to be a multi-hop reasoning problem, where models should progressively explore database elements, including tables, columns, and values, based on natural language input, while uncovering explicit or implicit relations @xcite . however, most existing methods modularize the schema linking process, treating the reasoning and generation ontological @xcite . for example, some approaches build connections between natural language and sql using graph models or context-aware training techniques @xcite @xcite @xcite , or employ intermediate representations to mitigate the impact of sql's nested structures @xcite @xcite . in addition, building schema linking modules with vector search or prompts for llms is another effective paradigm @xcite @xcite . while schema linking modules with high accuracy and recall can significantly reduce costs and simplify challenges, the reasoning and analytical capabilities inherent to llms, which have the potential to tackle more challenging problems, remain largely underexplored. therefore, we intentionally avoid using schema linking modules, despite their effectiveness, and instead frame the task as explicit or implicit reasoning problems as shown and introduced in figure 1 . in conclusion, we pivot on: ## linearly incremental sql translator list starts from the sql syntax tree, which is parsed and scheduled by the engine into an execution order. we will progressively provide: (1) how list produces verifiable single-hop reasoning steps (2) implementations of list with an incontext learning manner. ## experiments we will compare the effectiveness of our method with mainstream approaches that with or without the schema linking module. additionally, an ablation study is conducted to examine the impact of list decomposition and the verifier mode. we also present the coverage and cost analysis of list in appendix. b.2 and e. ## conclusion by decomposing the ast, list identifies a method to break down ontological sql generation tasks into single-hop reasoning steps, while ensuring that each step is grammatically verifiable. through a straightforward in-context learning approach, utilizing step-wise reasoning and step-wise generation, we achieved profound results using llm inference alone. furthermore, through ablation tests, list demonstrated the need and potential for integration with existing methods, which could shed some light on further rl-based methods. the rationale behind text-to-sql pipeline can be abstracted into two primary steps: schema linking and generation @xcite . a significant amount of methods focus on further decomposing this process and have proven effective.