让大模型用自然语言跨库跨接口查数据，更准更快。
Declarative Techniques for NL Queries over Heterogeneous Data
- 用声明式方法统一处理数据库和API查询
- 在混合数据源上准确率显著优于现有系统
- 适合需要对接多种数据源的工业级应用
在众多工业场景中，用户希望用自然语言提问，答案需整合来自多种结构化数据源的信息。随着大语言模型（LLM）的发展，系统可将自然语言问题转化为一组API或数据库调用，执行后合并结果生成自然语言回答。然而，这些系统在真实工业环境中仍不实用，因无法应对典型存在的数据源异构性。本文通过扩展流行的Spider基准数据集，引入需结合数据库与API调用的两个新任务，模拟真实工业环境的数据异构性。提出一种声明式方法来处理此类异构数据，并实证表明其在应对数据源异构方面显著优于当前最先进的基于LLM的代理或命令式代码生成系统。所构建的增强基准数据集已开放给研究社区使用。
原文摘要 · Abstract (English)
In many industrial settings, users wish to ask questions in natural language, the answers to which require assembling information from diverse structured data sources. With the advent of Large Language Models (LLMs), applications can now translate natural language questions into a set of API calls or database calls, execute them, and combine the results into an appropriate natural language response. However, these applications remain impractical in realistic industrial settings because they do not cope with the data source heterogeneity that typifies such environments. In this work, we simulate the heterogeneity of real industry settings by introducing two extensions of the popular Spider benchmark dataset that require a combination of database and API calls. Then, we introduce a declarative approach to handling such data heterogeneity and demonstrate that it copes with data source heterogeneity significantly better than state-of-the-art LLM-based agentic or imperative code generation systems. Our augmented benchmarks are available to the research community.
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