TANQ: An open domain dataset of table answered questions

TL;DR

TANQ introduces a novel open domain dataset for question answering that requires constructing tables from multiple sources, with detailed source attribution and benchmarking of current language models' performance.

Contribution

The paper presents TANQ, the first dataset for table-based question answering across multiple sources, and provides benchmark results and analysis of model capabilities and limitations.

Findings

Best model achieves 60.7 F1 score, 12.3 points below human performance.

Models struggle with multi-hop reasoning, math, and unit conversions.

TANQ reveals significant challenges in complex table-based question answering.

Abstract

Language models, potentially augmented with tool usage such as retrieval are becoming the go-to means of answering questions. Understanding and answering questions in real-world settings often requires retrieving information from different sources, processing and aggregating data to extract insights, and presenting complex findings in form of structured artifacts such as novel tables, charts, or infographics. In this paper, we introduce TANQ, the first open domain question answering dataset where the answers require building tables from information across multiple sources. We release the full source attribution for every cell in the resulting table and benchmark state-of-the-art language models in open, oracle, and closed book setups. Our best-performing baseline, Gemini Flash reaches an overall F1 score of 60.7, lagging behind human performance by 12.3 points. We analyse baselines' performance across different dataset attributes such as different skills required for this task, including multi-hop reasoning, math operations, and unit conversions. We further discuss common failures in model-generated answers, suggesting that TANQ is a complex task with many challenges ahead.