{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/learning-metadata-agnostic-representations","title":"Learning Metadata-Agnostic Representations for Text-to-SQL In-Context Example Selection","arxiv_id":"2410.14049","date":"2024-10-17","proceeding":null,"authors":["Chuhong Mai","Ro-ee Tal","Thahir Mohamed"],"abstract":"In-context learning (ICL) is a powerful paradigm where large language models (LLMs) benefit from task demonstrations added to the prompt. Yet, selecting optimal demonstrations is not trivial, especially for complex or multi-modal tasks where input and output distributions differ. We hypothesize that forming task-specific representations of the input is key. In this paper, we propose a method to align representations of natural language questions and those of SQL queries in a shared embedding space. Our technique, dubbed MARLO - Metadata-Agnostic Representation Learning for Text-tO-SQL - uses query structure to model querying intent without over-indexing on underlying database metadata (i.e. tables, columns, or domain-specific entities of a database referenced in the question or query). This allows MARLO to select examples that are structurally and semantically relevant for the task rather than examples that are spuriously related to a certain domain or question phrasing. When used to retrieve examples based on question similarity, MARLO shows superior performance compared to generic embedding models (on average +2.9\\%pt. in execution accuracy) on the Spider benchmark. It also outperforms the next best method that masks metadata information by +0.8\\%pt. in execution accuracy on average, while imposing a significantly lower inference latency.","url_abs":"https://arxiv.org/abs/2410.14049v1","url_pdf":"https://arxiv.org/pdf/2410.14049v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[],"tasks":[{"task_slug":"in-context-learning","task_name":"In-Context Learning"},{"task_slug":"question-similarity","task_name":"Question Similarity"},{"task_slug":"representation-learning","task_name":"Representation Learning"},{"task_slug":"text-to-sql","task_name":"Text to SQL"},{"task_slug":"text-to-sql","task_name":"Text-To-SQL"}],"methods":[{"method_slug":"align","method_name":"ALIGN"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/text-to-sql-on-spider","task":"Text-To-SQL","dataset":"spider","model":"MARLO + Claude 2.1","rank_in_archive_order":6,"of":20,"metrics":{"Execution Accuracy (Dev)":"83.6","Execution Accuracy (Test)":"84.0"},"uses_additional_data":false}],"syntology":{"syntology_url":"https://syntology.ai/paper/2410.14049","atlas_url":"https://app.syntology.ai/?focus=2410.14049","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}