2024/10/17 by Mai, Chuhong, Tal, Ro-ee, Thahir Mohamed +1 · 2 citations
Computer Science · #Advanced Database Systems and Queries #Artificial intelligence #Computation and Language (cs.CL) #Computer science #Context (archaeology) #Database #FOS: Computer and information sciences #History #Information retrieval #Metadata #Natural language processing #SQL #Selection (genetic algorithm) #Semantic Web and Ontologies #World Wide Web
paper · pdf · doi:10.48550/arxiv.2410.14049
openalex publication_date 2024/10/17 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.