ODTQA-FoRe: An Open-Domain Tabular Question Answering Dataset for Future Data Forecasting and Reasoning
Researchers have identified a critical gap in LLM-based tabular reasoning: most systems cannot forecast future values or reason about time-series trends. This work introduces ODTQA-FoRe, a dataset pairing historical real estate data with forward-looking queries, and TimeFore, an agent framework that decomposes the problem into specialized roles (Retriever, Forecaster, and reasoning components). The contribution matters because production systems increasingly need to answer questions like "Will this property appreciate?" rather than just "What was last quarter's price?" This signals growing demand for LLMs that bridge structured data retrieval, statistical forecasting, and interpretable reasoning, a capability gap that affects finance, real estate, and supply-chain applications.
Modelwire context
ExplainerThe key insight isn't just that LLMs fail at forecasting (that's known), but that the failure stems from conflating retrieval, statistical extrapolation, and reasoning into a single task. TimeFore's role-based decomposition suggests forecasting reliability improves when you separate the retriever from the forecaster from the explainer, rather than asking one model to do all three.
This directly validates the argument from Hugging Face's June piece on agent logic: enterprises need multi-step orchestration, not raw model scale. ODTQA-FoRe is a concrete instantiation of that principle applied to time-series reasoning. The dataset also echoes a pattern from the weather forecasting story (Windborne outperforming government models) where domain-specific architectures plus historical data beat general approaches, though here the domain is tabular reasoning rather than meteorology.
If TimeFore's performance on ODTQA-FoRe holds when tested on unseen real estate markets or other tabular domains (supply chain, financial forecasting), that confirms the role decomposition generalizes. If it only works on the benchmark domain, the contribution is narrower than claimed. Expect follow-up work within six months testing transfer.
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MentionsODTQA-FoRe · TimeFore · LLM
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