# TaCube: Pre-computing Data Cubes for Answering Numerical-Reasoning Questions over Tabular Data

arXiv: https://arxiv.org/abs/2205.12682 · HTML: https://mlsanity.com/paper/2205.12682/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- Placed in Knowledge- and tool-augmented reasoning, one of 17 directions in LLM reasoning.
- Builds on 9 of its 34 references that are in the map.
- First posted on arXiv 25 May 2022.

## Placed in

- LLM reasoning / [Knowledge- and tool-augmented reasoning](https://mlsanity.com/lr/knowledge-and-tool-augmented-reasoning/)

## Builds on (most cited first, 9 of 9)

- [Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning](https://mlsanity.com/paper/1709.00103/) (2017)
- [TAPEX: Table Pre-training via Learning a Neural SQL Executor](https://mlsanity.com/paper/2107.07653/) (2021)
- [UnifiedSKG: Unifying and Multi-Tasking Structured Knowledge Grounding with Text-to-Text Language Models](https://mlsanity.com/paper/2201.05966/) (2022)
- [HiTab: A Hierarchical Table Dataset for Question Answering and Natural Language Generation](https://mlsanity.com/paper/2108.06712/) (2021)
- [Table Pre-training: A Survey on Model Architectures, Pre-training Objectives, and Downstream Tasks](https://mlsanity.com/paper/2201.09745/) (2022)
- [FORTAP: Using Formulas for Numerical-Reasoning-Aware Table Pretraining](https://mlsanity.com/paper/2109.07323/) (2021)
- [Augmenting Pre-trained Language Models with QA-Memory for Open-Domain Question Answering](https://mlsanity.com/paper/2204.04581/) (2022)
- [IncSQL: Training Incremental Text-to-SQL Parsers with Non-Deterministic Oracles](https://mlsanity.com/paper/1809.05054/) (2018)
- [A Hybrid Semantic Parsing Approach for Tabular Data Analysis](https://mlsanity.com/paper/1910.10363/) (2019)

## Built on by (most cited first, 19 of 19)

- [LEVER: Learning to Verify Language-to-Code Generation with Execution](https://mlsanity.com/paper/2302.08468/) (2023)
- [Binding Language Models in Symbolic Languages](https://mlsanity.com/paper/2210.02875/) (2022)
- [Large Language Models are Versatile Decomposers: Decompose Evidence and Questions for Table-based Reasoning](https://mlsanity.com/paper/2301.13808/) (2023)
- [PACIFIC: Towards Proactive Conversational Question Answering over Tabular and Textual Data in Finance](https://mlsanity.com/paper/2210.08817/) (2022)
- [MultiTabQA: Generating Tabular Answers for Multi-Table Question Answering](https://mlsanity.com/paper/2305.12820/) (2023)
- [ReasTAP: Injecting Table Reasoning Skills During Pre-training via Synthetic Reasoning Examples](https://mlsanity.com/paper/2210.12374/) (2022)
- [OpenTab: Advancing Large Language Models as Open-domain Table Reasoners](https://mlsanity.com/paper/2402.14361/) (2024)
- [ProTrix: Building Models for Planning and Reasoning over Tables with Sentence Context](https://mlsanity.com/paper/2403.02177/) (2024)
- [From Zero to Hero: Examining the Power of Symbolic Tasks in Instruction Tuning](https://mlsanity.com/paper/2304.07995/) (2023)
- [SEER : A Knapsack approach to Exemplar Selection for In-Context HybridQA](https://mlsanity.com/paper/2310.06675/) (2023)
- [A Survey on Table-and-Text HybridQA: Concepts, Methods, Challenges and Future Directions](https://mlsanity.com/paper/2212.13465/) (2022)
- [KET-QA: A Dataset for Knowledge Enhanced Table Question Answering](https://mlsanity.com/paper/2405.08099/) (2024)
- [QTSumm: Query-Focused Summarization over Tabular Data](https://mlsanity.com/paper/2305.14303/) (2023)
- [Training Table Question Answering via SQL Query Decomposition](https://mlsanity.com/paper/2402.13288/) (2024)
- [PanelTR: Zero-Shot Table Reasoning Framework Through Multi-Agent Scientific Discussion](https://mlsanity.com/paper/2508.06110/) (2025)
- [LyS at SemEval 2025 Task 8: Zero-Shot Code Generation for Tabular QA](https://mlsanity.com/paper/2508.09012/) (2025)
- [MRT at IberLEF-2025 PRESTA Task: Maximizing Recovery from Tables with Multiple Steps](https://mlsanity.com/paper/2507.12981/) (2025)
- [DeALOG: Decentralized Multi-Agents Log-Mediated Reasoning Framework](https://mlsanity.com/paper/2602.00996/) (2026)
- [Evolving from Lessons: Skill-Augmented Table Graph Reasoning for Operation-wise Table Question Answering](https://mlsanity.com/paper/2607.22633/) (2026)

## Abstract (arXiv)

Existing auto-regressive pre-trained language models (PLMs) like T5 and BART, have been well applied to table question answering by UNIFIEDSKG and TAPEX, respectively, and demonstrated state-of-the-art results on multiple benchmarks. However, auto-regressive PLMs are challenged by recent emerging numerical reasoning datasets, such as TAT-QA, due to the error-prone implicit calculation. In this paper, we present TaCube, to pre-compute aggregation/arithmetic results for the table in advance, so that they are handy and readily available for PLMs to answer numerical reasoning questions. TaCube systematically and comprehensively covers a collection of computational operations over table segments. By simply concatenating TaCube to the input sequence of PLMs, it shows significant experimental effectiveness. TaCube promotes the F1 score from 49.6% to 66.2% on TAT-QA and achieves new state-of-the-art results on WikiTQ (59.6% denotation accuracy). TaCube's improvements on numerical reasoning cases are even more notable: on TAT-QA, TaCube promotes the exact match accuracy of BART-large by 39.6% on sum, 52.5% on average, 36.6% on substraction, and 22.2% on division. We believe that TaCube is a general and portable pre-computation solution that can be potentially integrated to various numerical reasoning frameworks