You Only Read Once (YORO): Learning to Internalize Database Knowledge for Text-to-SQL

Hideo Kobayashi, Wuwei Lan, Peng Shi, Shuaichen Chang, Jiang Guo, Henghui Zhu, Zhiguo Wang, Patrick Ng
{"title":"You Only Read Once (YORO): Learning to Internalize Database Knowledge for Text-to-SQL","authors":"Hideo Kobayashi, Wuwei Lan, Peng Shi, Shuaichen Chang, Jiang Guo, Henghui Zhu, Zhiguo Wang, Patrick Ng","doi":"arxiv-2409.12172","DOIUrl":null,"url":null,"abstract":"While significant progress has been made on the text-to-SQL task, recent\nsolutions repeatedly encode the same database schema for every question,\nresulting in unnecessary high inference cost and often overlooking crucial\ndatabase knowledge. To address these issues, we propose You Only Read Once\n(YORO), a novel paradigm that directly internalizes database knowledge into the\nparametric knowledge of a text-to-SQL model during training and eliminates the\nneed for schema encoding during inference. YORO significantly reduces the input\ntoken length by 66%-98%. Despite its shorter inputs, our empirical results\ndemonstrate YORO's competitive performances with traditional systems on three\nbenchmarks as well as its significant outperformance on large databases.\nFurthermore, YORO excels in handling questions with challenging value\nretrievals such as abbreviation.","PeriodicalId":501030,"journal":{"name":"arXiv - CS - Computation and Language","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"arXiv - CS - Computation and Language","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/arxiv-2409.12172","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

Abstract

While significant progress has been made on the text-to-SQL task, recent solutions repeatedly encode the same database schema for every question, resulting in unnecessary high inference cost and often overlooking crucial database knowledge. To address these issues, we propose You Only Read Once (YORO), a novel paradigm that directly internalizes database knowledge into the parametric knowledge of a text-to-SQL model during training and eliminates the need for schema encoding during inference. YORO significantly reduces the input token length by 66%-98%. Despite its shorter inputs, our empirical results demonstrate YORO's competitive performances with traditional systems on three benchmarks as well as its significant outperformance on large databases. Furthermore, YORO excels in handling questions with challenging value retrievals such as abbreviation.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
只读一次 (YORO):学习内化数据库知识,实现文本到 SQL 的转换
虽然文本到 SQL 任务已经取得了重大进展,但最近的解决方案对每个问题都重复编码相同的数据库模式,导致不必要的高推理成本,而且经常忽略关键的数据库知识。为了解决这些问题,我们提出了 "只读一次"(YORO)这一新颖的范式,它能在训练过程中将数据库知识直接内化到文本到 SQL 模型的参数知识中,而无需在推理过程中进行模式编码。YORO 将输入令牌的长度大幅减少了 66%-98%。此外,YORO 在处理缩写等具有挑战性的数值检索问题时表现出色。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
LLMs + Persona-Plug = Personalized LLMs MEOW: MEMOry Supervised LLM Unlearning Via Inverted Facts Extract-and-Abstract: Unifying Extractive and Abstractive Summarization within Single Encoder-Decoder Framework Development and bilingual evaluation of Japanese medical large language model within reasonably low computational resources Human-like Affective Cognition in Foundation Models
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1