做表格问答或时序预测的团队终于有了专门的数据集和框架——TimeFore用LLM+外部模型解决了LLM本身预测不准的痛点，做数据分析和AI应用的开发者可以直接参考其协作架构。
现有表格问答系统多聚焦于历史数据查询，无法进行面向未来的数值预测。为此，研究者提出了新任务——开放域表格问答的未来数据预测与推理，并构建了首个基于房地产数据的时序预测与推理数据集ODTQA-FoRe。该任务面临历史数据精准检索、LLM预测能力不足、多样化查询标准化回答等挑战。为解决这些问题，团队提出了TimeFore框架，将问题分解为检索器、预测器和分析器三个协作角色，分别负责SQL数据获取、外部时序模型调用和结果综合。实验表明，TimeFore在预测准确性和回答一致性上显著优于基线方法。
ODTQA-FoRe: An Open-Domain Tabular Question Answering Dataset for Future Data Forecasting and Reasoning
The rapid development of LLMs has significantly advanced tabular question answering, but most systems cannot perform future-oriented numerical prediction. To address this gap, we introduce a novel task, Open-Domain Tabular Question Answering for Future Data Forecasting and Reasoning, and propose the first dataset to cover time-series forecasting and forecast-based reasoning scenarios using real estate data. This task poses challenges in retrieving precise historical data, overcoming the forecasting limitations of LLMs, and standardizing responses for diverse queries. To solve the above challenges, we propose TimeFore, an LLM agent-based framework that decomposes the problem into three collaborative roles: a Retriever autonomously generates SQL to fetch data, a Forecaster invokes external time-series models for higher accuracy, and an Analyzer synthesizes the results to construct a precise and consistent final answer. Extensive experiments demonstrate the effectiveness of our TimeFore.