Problem
Research questions and friction points this paper is trying to address.
Business Intelligence
Large Language Models
Data Preparation
End-to-End
BI-Agent and BI-Bench: Towards Automating End-to-End Business Intelligence
🤖 AI Summary
研究通过构建BI-Bench基准和设计BI-Agent工具，利用大语言模型解决商业智能中数据准备复杂耗时的问题，提高了回答BI问题的准确性。
📝 Abstract
Business intelligence (BI) is a cornerstone of enterprise decision-making and is widely used by enterprise users in software such as Power BI and Tableau. In traditional BI workflows, users need to prepare data by (1) identifying relevant tables, (2) performing data transformations, and (3) building join relationships, before they can (4) answer their business questions. These steps can be complex and time-consuming, making BI challenging.
Given the strong capabilities of large language models (LLMs) in working with data, we study their ability to answer BI questions end-to-end, without requiring users to manually perform the tedious preparation steps. To do this, we harvest a large collection of real-world BI projects from public sources, and manually extract pairs of (questions, ground-truth answers) from real user dashboards. The resulting benchmark, BI-Bench, is the first benchmark to systematically study LLMs' ability on end-to-end BI.
We find that even frontier LLMs perform poorly on BI-Bench, with less than 50% accuracy. To address their limitations, we design a tool-augmented BI-Agent that decomposes BI workflows into subtasks on structured data, such as search, join, and transform, and orchestrates specialized data management methods across BI stages. Furthermore, we develop a post-training framework that synthesizes training trajectories from real BI projects, enabling BI-Agent to be further post-trained using both supervised fine-tuning (SFT) and reinforcement learning (RL). BI-Agent achieves substantial accuracy gains of up to 40 percentage points with vanilla LLMs, and post-trained BI-Agent yields gains of up to 30 points. Our results highlight the importance of combining tool-augmented reasoning with domain-specific post-training in complex BI workflows, and point to promising directions for future research.
Innovation
Methods, ideas, or system contributions that make the work stand out.
tool-augmented BI-Agent
end-to-end Business Intelligence
post-training framework
supervised fine-tuning (SFT)
reinforcement learning (RL)
🔎 Similar Papers
No similar papers found.
