ODTQA-FoRe: An Open-Domain Tabular Question Answering Dataset for Future Data Forecasting and Reasoning

2026-06-01 · Source: Artificial Intelligence · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics · Depth: Expert, quick

Summary

ODTQA-FoRe introduces a novel Open-Domain Tabular Question Answering task and the first dataset specifically designed for future data forecasting and reasoning. This dataset utilizes real estate data to cover time-series forecasting and forecast-based reasoning scenarios, addressing the current limitation of most LLM-based tabular QA systems in performing future-oriented numerical predictions. The task presents challenges in precise historical data retrieval, overcoming LLM forecasting limitations, and standardizing diverse query responses. To tackle these, the TimeFore framework, an LLM agent-based system, decomposes the problem into three collaborative roles: a Retriever for SQL-based data fetching, a Forecaster for external time-series model invocation, and an Analyzer for synthesizing results into precise, consistent answers. Experiments confirm TimeFore's effectiveness.

Key takeaway

For AI Scientists developing advanced tabular QA systems, you should explore integrating specialized agent frameworks like TimeFore to overcome LLM limitations in future-oriented numerical prediction. Consider leveraging the ODTQA-FoRe dataset for training and evaluation, particularly when dealing with time-series forecasting and reasoning over structured data. This approach allows for more accurate and consistent responses by combining LLM reasoning with external forecasting models.

Key insights

A new dataset and LLM agent framework enable future-oriented tabular question answering by integrating time-series forecasting.

Principles

• Decompose complex QA into specialized agent roles.
• Integrate external models for specialized tasks like forecasting.
• Standardize responses for diverse queries.

Method

TimeFore uses a Retriever (SQL data fetch), a Forecaster (external time-series models), and an Analyzer (synthesizes results) to answer future-oriented tabular questions.

In practice

• Apply agent-based LLMs to real estate data forecasting.
• Combine LLMs with traditional time-series models.
• Develop SQL generation for data retrieval.

Topics

• Tabular Question Answering
• Time-Series Forecasting
• LLM Agents
• ODTQA-FoRe Dataset
• TimeFore Framework
• Real Estate Data

Best for: Research Scientist, AI Scientist, Machine Learning Engineer, AI Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Artificial Intelligence.