LitE-SQL: A Lightweight and Efficient Text-to-SQL Framework with Vector-based Schema Linking and Execution-Guided Self-Correction (2026.findings-eacl)
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| Challenge: | Existing methods rely on proprietary models to generate SQL queries. |
| Approach: | They propose a lightweight framework that translates natural language questions into SQL queries. |
| Outcome: | The proposed framework achieves 72.10% execution accuracy on BIRD and 88.45% on Spider 1.0 . it offers a practical solution for privacy-sensitive and resource-constrained settings. |
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| Challenge: | Existing approaches depend on static, pre-processed database information, which restricts the model’s capacity to deeply comprehend the underlying database content. |
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| Challenge: | Existing text-to-SQL approaches have overlooked the critical aspect of system robustness. |
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| Challenge: | Text-to-SQL aims to convert natural language questions into executable SQL queries. |
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| Challenge: | Existing evaluation methods for text-to-SQL systems show many false positives and negatives . however, the Execution Accuracy (EX) metric is flawed and can diverge from human experts. |
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| Challenge: | Recent studies have shifted paradigms and leveraged Large Language Models (LLMs) to tackle the challenging task of Text-to-SQL. |
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SchemaGraphSQL: Efficient Schema Linking with Pathfinding Graph Algorithms for Text-to-SQL on Large-Scale Databases (2026.findings-eacl)
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AmirHossein Safdarian, Milad Mohammadi, Ehsan Jahanbakhsh Bashirloo, Mona Shahamat Naderi, Heshaam Faili
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| Challenge: | relying on proprietary Large Language Models poses privacy and cost implications for models. |
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| Challenge: | Large Language Models (LLMs) enable natural language to SQL conversion, but generating accurate, efficient queries is challenging due to ambiguous intent, domain knowledge requirements and database constraints. |
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| Challenge: | Recent advances in Large Language Models have improved Text-to-SQL methods . however, they still face challenges such as complex multi-stage pipelines and poor robustness to noisy schema information. |
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