首个面向未来数据预测的表格问答数据集，助力大模型做时间序列推理。
ODTQA-FoRe: An Open-Domain Tabular Question Answering Dataset for Future Data Forecasting and Reasoning
- 设计三角色协作框架，分步完成查数、预测与分析。
- 在真实房产数据上实现精准未来数值预测，准确率超基线18.3%。
- 适合研究时序推理、大模型预测能力的学者与工程师。
大模型虽推动了表格问答发展，但多数系统无法进行未来导向的数值预测。为此，我们提出新任务——开放域表格问答中的未来数据预测与推理，并构建首个涵盖时间序列预测与基于预测推理场景的真实房产数据集。该任务面临精准历史数据检索、突破大模型预测瓶颈及多样化查询应答标准化等挑战。为此，我们提出基于大模型智能体的TimeFore框架，将问题分解为三个协同角色：检索器自主生成SQL获取数据，预测器调用外部时间序列模型提升精度，分析器综合结果生成精确一致的答案。大量实验验证了TimeFore的有效性。
原文摘要 · Abstract (English)
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.
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