圆桌：利用动态模式和上下文自动完成提高表格问答中的查询精度

RoundTable: Leveraging Dynamic Schema and Contextual Autocomplete for Enhanced Query Precision in Tabular Question Answering

August 22, 2024

作者: Pratyush Kumar, Kuber Vijaykumar Bellad, Bharat Vadlamudi, Aman Chadha

cs.AI

摘要

随着大型语言模型（LLMs）的进步，一个重要的应用案例是用普通英语查询数据库，将用户问题翻译成可执行的数据库查询，这方面取得了显著进展。然而，现实世界中的数据集往往具有大量属性和复杂值，使得LLMs准确识别自然语言查询中相关列或值的任务变得复杂。传统方法无法充分传达数据集的规模和复杂性给LLM。为了解决这些挑战，我们提出了一个新颖的框架，利用输入表格上的全文搜索（FTS）。这种方法不仅能够精确检测特定值和列，还能缩小语言模型的搜索空间，从而提高查询准确性。此外，它支持自定义自动完成功能，根据表中的数据提供查询建议。这种集成显著改进了用户与复杂数据集之间的交互，为当前表查询能力所面临的局限性提供了一个复杂的解决方案。这项工作附带了适用于Mac和Windows平台的应用程序，读者可以在自己的数据上尝试。

English

With advancements in Large Language Models (LLMs), a major use case that has emerged is querying databases in plain English, translating user questions into executable database queries, which has improved significantly. However, real-world datasets often feature a vast array of attributes and complex values, complicating the LLMs task of accurately identifying relevant columns or values from natural language queries. Traditional methods cannot fully relay the datasets size and complexity to the LLM. To address these challenges, we propose a novel framework that leverages Full-Text Search (FTS) on the input table. This approach not only enables precise detection of specific values and columns but also narrows the search space for language models, thereby enhancing query accuracy. Additionally, it supports a custom auto-complete feature that suggests queries based on the data in the table. This integration significantly refines the interaction between the user and complex datasets, offering a sophisticated solution to the limitations faced by current table querying capabilities. This work is accompanied by an application for both Mac and Windows platforms, which readers can try out themselves on their own data.