让大模型无缝访问数据库和API，解决跨源查询难题
A System and Benchmark for LLM-based Q&A on Heterogeneous Data
- 构建siwarex平台，统一处理数据库与API的自然语言查询
- 在Spider数据集上引入API接口，验证平台跨源能力
- 为研究者提供新基准，推动工业级多源问答发展
在众多工业场景中，用户希望用自然语言提问，答案可能存在于电子表格、数据库、API或其组合中。但用户常难以定位或访问正确数据源，尤其当多个孤立数据源需协同时问题更严重。尽管近期基于大模型的Text-to-SQL应用已部分缓解此问题，但在真实工业环境中仍不实用，因无法应对数据源异构性。本文提出siwarex平台，实现对数据库与API的无缝自然语言访问。为验证其效果，我们扩展了流行的Spider数据集与评测基准，将其中部分表替换为数据获取API。实验表明，siwarex能有效应对数据源异构性。该改进版Spider基准将很快向研究社区开放。
原文摘要 · Abstract (English)
In many industrial settings, users wish to ask questions whose answers may be found in structured data sources such as a spreadsheets, databases, APIs, or combinations thereof. Often, the user doesn't know how to identify or access the right data source. This problem is compounded even further if multiple (and potentially siloed) data sources must be assembled to derive the answer. Recently, various Text-to-SQL applications that leverage Large Language Models (LLMs) have addressed some of these problems by enabling users to ask questions in natural language. However, these applications remain impractical in realistic industrial settings because they fail to cope with the data source heterogeneity that typifies such environments. In this paper, we address heterogeneity by introducing the siwarex platform, which enables seamless natural language access to both databases and APIs. To demonstrate the effectiveness of siwarex, we extend the popular Spider dataset and benchmark by replacing some of its tables by data retrieval APIs. We find that siwarex does a good job of coping with data source heterogeneity. Our modified Spider benchmark will soon be available to the research community
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