圓桌：利用動態架構和情境自動完成提升表格問答中的查詢精確度

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

隨著大型語言模型（LLMs）的進步，一個主要的應用案例是以純英語查詢數據庫，將用戶問題翻譯為可執行的數據庫查詢，這方面取得了顯著進展。然而，現實世界的數據集往往包含眾多屬性和複雜值，使得LLMs準確識別自然語言查詢中相關列或值變得複雜。傳統方法無法充分傳達數據集的規模和複雜性給LLM。為應對這些挑戰，我們提出了一個利用輸入表格上的全文搜索（FTS）的新框架。這種方法不僅能夠精確檢測特定值和列，還能縮小語言模型的搜索範圍，從而提高查詢準確性。此外，它支持自定義自動完成功能，根據表格中的數據提供查詢建議。這種整合顯著改進了用戶與複雜數據集之間的互動，為目前表格查詢功能所面臨的限制提供了一個複雜的解決方案。這項工作附帶了一個適用於Mac和Windows平台的應用程序，讀者可以在自己的數據上試用。

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.