title: Synthesizing Text-to-SQL Data from Weak and Strong LLMs
summary: The capability gap between open-source and closed-source large language
  models (LLMs) remains a challenge in text-to-SQL tasks. In this paper, we
  introduce a synthetic data approach that combines data produced by larger, more
  powerful models (strong models) with error information data generated by
  smaller, not well-aligned models (weak models). The method not only enhances
  the domain generalization of text-to-SQL models but also explores the potential
  of error data supervision through preference learning. Furthermore, we employ
  the synthetic data approach for instruction tuning on open-source LLMs,
  resulting SENSE, a specialized text-to-SQL model. The effectiveness of SENSE is
  demonstrated through state-of-the-art results on the SPIDER and BIRD
  benchmarks, bridging the performance gap between open-source models and methods
  prompted by closed-source models.
paper_authors._id: 66b322a8cbe0b82f9573baa4
paper_authors.name: Jiaxi Yang
paper_authors._id: 66b322a8cbe0b82f9573baa5
paper_authors.name: Binyuan Hui
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-08-14T13:06:06.668Z
paper_authors._id: 61e4c4ca1ab24785ac11ba69
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/61e4c4ca1ab24785ac11ba69/1Q1zhhyGSJ9RJG9MzwxVv.jpeg
paper_authors.fullname: Binyuan Hui
paper_authors.type: user
paper_authors.user: huybery
paper_authors._id: 66b322a8cbe0b82f9573baa6
paper_authors.name: Min Yang
paper_authors._id: 66b322a8cbe0b82f9573baa7
paper_authors.name: Jian Yang
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-10-14T19:05:11.130Z
paper_authors._id: 64ccb9bfead94891d12aef42
paper_authors.avatarUrl: /avatars/c54809d43d93d3f0766bd2555cecc4e3.svg
paper_authors.fullname: Yang Jian
paper_authors.type: user
paper_authors.user: CSJianYang
paper_authors._id: 66b322a8cbe0b82f9573baa8
paper_authors.name: Junyang Lin
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-08-14T13:06:14.095Z
paper_authors._id: 620760a26e3b7210c2ff1943
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/620760a26e3b7210c2ff1943/VC-rKqimF6yxGESNVlPoR.jpeg
paper_authors.fullname: Junyang Lin
paper_authors.type: user
paper_authors.user: JustinLin610
paper_authors._id: 66b322a8cbe0b82f9573baa9
paper_authors.name: Chang Zhou
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-08-14T13:06:34.661Z
paper_authors._id: 622892774323cef93a956a4a
paper_authors.avatarUrl: /avatars/e57ef5c3b0c4289988ccd42f14e54336.svg
paper_authors.fullname: chang zhou
paper_authors.type: user
paper_authors.user: jiemizc
paper_title: Synthesizing Text-to-SQL Data from Weak and Strong LLMs
paper_summary: The capability gap between open-source and closed-source large language
  models (LLMs) remains a challenge in text-to-SQL tasks. In this paper, we
  introduce a synthetic data approach that combines data produced by larger, more
  powerful models (strong models) with error information data generated by
  smaller, not well-aligned models (weak models). The method not only enhances
  the domain generalization of text-to-SQL models but also explores the potential
  of error data supervision through preference learning. Furthermore, we employ
  the synthetic data approach for instruction tuning on open-source LLMs,
  resulting SENSE, a specialized text-to-SQL model. The effectiveness of SENSE is
  demonstrated through state-of-the-art results on the SPIDER and BIRD
  benchmarks, bridging the performance gap between open-source models and methods
  prompted by closed-source models.
paper_ai_keywords: large language models (LLMs) text-to-SQL tasks synthetic models weak models error information data synthetic data approach domain generalization error data supervision acoustic learning instruction tuning SENSE text-to-SQL model SPIDER benchmark BIRD benchmark closed-source models