ODTQA-FoRe: An Open-Domain Tabular Question Answering Dataset for Future Data Forecasting and Reasoning
arXiv:2606.02433v1 Announce Type: cross Abstract: 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 respons
The rapid advancement and limitations of current LLMs prompt the development of more sophisticated data handling and reasoning capabilities, especially for future-oriented tasks.
Improving LLM ability to forecast and reason with time-series data is crucial for their application in financial, economic, and strategic planning, moving beyond static knowledge bases.
LLMs are evolving from primarily historical data recall to systems capable of predictive analytics and reasoning over future scenarios, significantly expanding their utility.
- · AI developers
- · Financial institutions
- · Real estate analytics
- · Data scientists
- · Traditional forecasting models
- · Businesses relying solely on static data models
LLMs can now perform open-domain tabular question answering that includes future data forecasting.
This capability allows for more dynamic and predictive business intelligence tools, reducing reliance on human experts for initial forecasts.
The integration of such sophisticated forecasting into autonomous AI agents could lead to more proactive and self-optimizing systems across various industries.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.CL