[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-86072-en":3,"doc-seo-86072-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":13,"seo_description":14,"update_tm":28,"read_time":29},86072,687197207057,"Sage","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","The Nuts and Bolts of Natural Language to SQL Translation","Natural Language to SQL (NL2SQL) translation remains a challenging open problem despite the rise of large language models. The work analyzes how multiple NL2SQL pipeline extensions interact, aiming to enable stronger results with lighter, domain-aligned models. It combines NatSQL as an intermediate representation, adds a preprocessing stage and a synthetic-data fine-tuning step, and introduces a final beam reranker. An ablation study and Shapley analysis quantify component contributions across SmBoP and RASAT backbones.","THE NUTS AND BOLTS OF NATURAL LANGUAGE TO SQL TRANSLATION: A SYSTEMATIC ANALYSIS OF MODEL PIPELINE OPTIMISATION APPROACHES AND THEIR INTERACTIONS  \narXiv :2607 . 109 1 1v 1 [ cs .CL] 12 Jul 2026  \nFilip Klubika  \nADAPT Research Centre Trinity College Dublin Dublin, Ireland [fklubicka@gmail.com](fklubicka@gmail.com)  \nVasudevan Nedumpozhimana  \nADAPT Research Centre Trinity College Dublin Dublin, Ireland [vnedumpo@tcd.ie](vnedumpo@tcd.ie)  \nSneha Rautmare  \nADAPT Research Centre Trinity College Dublin Dublin, Ireland  \n[sneha.rautmare@adaptcentre.ie](sneha.rautmare@adaptcentre.ie)  \nBora Caglayan  \nHuawei Ireland Research Centre Dublin, Ireland [bora.caglayan@huawei.com](bora.caglayan@huawei.com)  \nMingxue Wang  \nHuawei Ireland Research Centre Dublin, Ireland [wangmingxue1@huawei.com](wangmingxue1@huawei.com)  \nJohn D. Kelleher  \nADAPT Research Centre Trinity College Dublin Dublin, Ireland [john.kelleher@tcd.ie](john.kelleher@tcd.ie)  \nABSTRACT  \nIn the age of large language models, Natural Language to SQL (NL2SQL) translation remains an open problem with many useful applications. We explore interactions between several NL2SQL pipeline extensions to inspire development of more lightweight models. Specifically, we integrate the NatSQL intermediate representation, include a preprocessing step and a fine-tuning step based on synthetic data, and develop a novel reranker model to improve SQL selection in the final beam.  \nWe perform an ablation study supplemented by a Shapley analysis of these different components integrated with two backbone architectures, SmBoP and RASAT. We find that simply combining all of them does not lead to best results, but that their impact depends on their interactions with the baseline system, as well as each other.  \nKeywords NL2SQL · SQL · reranker · Spider · generative models · data augmentation · optimization methods · model architectures · code generation  \n1 Introduction  \nNatural Language to SQL translation (NL2SQL) is a subtask of the broader problem of semantic parsing, defined as understanding the meaning of natural language utterances and mapping them to meaningful executable queries. Solving NL2SQL is a problem with practical applications such as allowing integration of natural language interfaces to relational database management systems, enhancing accessibility and improving user experience. In an NL2SQL scenario, given a relational database and a natural language question (NLQ), the goal is to find an equivalent SQL query which will answer the NLQ once executed. The core challenge stems from understanding the meaning and intention of the NLQ, which can be expressed in myriad ways due to the high complexity and expressiveness of natural language, and translating that meaning into an SQL query, which is comparably more narrow in scope and follows significantly simpler and more rigidly defined syntactic and semantic rules. Thus NL2SQL is still considered an open problem.  \nAs LLMs grow ever larger and more capable, it becomes more tempting to apply them to any given task. However this is not always necessary, feasible or responsible in many real-world applications or production environments, with the models being either too large, too expensive or too power-hungry to realistically deploy. We argue that domain-aligned, specialised NL2SQL systems have yet to plateau, and we explore ways to improve performance of more light-weight models. To this end, we carry out a systematic analysis of several NL2SQL pipeline extensions which have been  \nThe Nuts and Bolts of Natural Language to SQL Translation  \nFigure 1: Points of optimisation in an NL2SQL pipeline.  \nshown to improve performance so as to understand their relative benefit as well as the benefits of combining them. Each of these extensions was selected to represent a state of the art intervention at a different stage in the NL2SQL pipeline, as shown in Figure 1 . We study the following extensions: using intermediate representations, syntheti","cbCaibjB12v5ELxp","https://ap.wps.com/l/cbCaibjB12v5ELxp","pdf",404964,3,1,20,"English","en",105,"# Introduction\n# Related Work","[{\"question\":\"What problem does the paper address?\",\"answer\":\"The paper studies Natural Language to SQL (NL2SQL) translation, focusing on how to improve model performance while keeping models lightweight and suitable for real deployments.\"},{\"question\":\"Which NL2SQL pipeline extensions are integrated in the study?\",\"answer\":\"It integrates NatSQL as an intermediate representation, adds preprocessing and a synthetic-data fine-tuning step, and develops a novel final beam reranker to improve SQL selection.\"},{\"question\":\"How is the benefit of each component measured?\",\"answer\":\"The paper uses an ablation study together with a Shapley analysis to quantify each component’s contribution and to examine interaction effects across two backbone architectures.\"}]",1784208316,50,{"code":4,"msg":31,"data":32},"ok",{"site_id":25,"language":24,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"the-nuts-and-bolts-of-natural-language-to-sql-translation","",{"@graph":36,"@context":85},[37,53,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,50],{"item":41,"name":42,"@type":43,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":20},"https://docshare.wps.com/document/research-report/",{"item":51,"name":13,"@type":43,"position":52},"https://docshare.wps.com/document/the-nuts-and-bolts-of-natural-language-to-sql-translation/86072/",4,{"url":51,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":24,"description":14,"dateModified":61,"datePublished":62,"encodingFormat":60,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":41,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-07-27","2026-07-16",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What problem does the paper address?","Question",{"text":75,"@type":76},"The paper studies Natural Language to SQL (NL2SQL) translation, focusing on how to improve model performance while keeping models lightweight and suitable for real deployments.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which NL2SQL pipeline extensions are integrated in the study?",{"text":80,"@type":76},"It integrates NatSQL as an intermediate representation, adds preprocessing and a synthetic-data fine-tuning step, and develops a novel final beam reranker to improve SQL selection.",{"name":82,"@type":73,"acceptedAnswer":83},"How is the benefit of each component measured?",{"text":84,"@type":76},"The paper uses an ablation study together with a Shapley analysis to quantify each component’s contribution and to examine interaction effects across two backbone architectures.","https://schema.org",{"og:url":51,"og:type":87,"og:title":13,"og:site_name":58,"og:description":14},"article",{"robots":89,"canonical":51},"index,follow",{"doc_id":7,"site_id":25},{"code":4,"msg":5,"data":92},[93,97,101,105,110,114,119,122,126,129,133],{"id":21,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":52,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":29,"slug":113},6,"Technology","technology",{"id":115,"doc_module":4,"doc_module_name":46,"category_name":116,"show_sort_weight":117,"slug":118},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":120,"slug":121},30,"research-report",{"id":123,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":22,"slug":125},9,"Religion & Spirituality","religion-spirituality",{"id":22,"doc_module":4,"doc_module_name":46,"category_name":127,"show_sort_weight":22,"slug":128},"World Cup","world-cup",{"id":130,"doc_module":4,"doc_module_name":46,"category_name":131,"show_sort_weight":130,"slug":132},10,"Lifestyle","lifestyle",{"id":134,"doc_module":4,"doc_module_name":46,"category_name":135,"show_sort_weight":106,"slug":136},19,"General","general"]