FORTAP: Using Formulas for Numerical-Reasoning-Aware Table Pretraining (2022.acl-long)
Copied to clipboard
| Challenge: | Tables store rich numerical data, but numerical reasoning over tables is still a challenge. |
| Approach: | They propose a spreadsheet formula is a valuable supervision for numerical reasoning in tables. |
| Outcome: | The proposed method outperforms state-of-the-art methods on three representative datasets of formula prediction, question answering, and cell type classification. |
Similar Papers
NL2Formula: Generating Spreadsheet Formulas from Natural Language Queries (2024.findings-eacl)
Copied to clipboard
Wei Zhao, Zhitao Hou, Siyuan Wu, Yan Gao, Haoyu Dong, Yao Wan, Hongyu Zhang, Yulei Sui, Haidong Zhang
| Challenge: | Creating spreadsheet formulas remains a tedious and error-prone task for many end-users . a novel task is proposed to generate spreadsheet formulae from a user's NL query . |
| Approach: | They propose a task to generate formulas that are grounded on a spreadsheet table given a Natural Language query as input. |
| Outcome: | The proposed task generates formulas that are grounded on a spreadsheet table, given a natural language query as input. |
Towards Table-to-Text Generation with Numerical Reasoning (2021.acl-long)
Copied to clipboard
| Challenge: | Recent studies have shown improvement in generating descriptive text from structured data. |
| Approach: | They propose a framework for numerical table-to-text generation based on numerical reasoning . they use a pre-trained model and a copy mechanism to fine-tune the models to produce fluent text . |
| Outcome: | The proposed framework lacks fidelity to the table contents and is based on a pre-trained model and a copy mechanism. |
Structural Encoding and Pre-training Matter: Adapting BERT for Table-Based Fact Verification (2021.eacl-main)
Copied to clipboard
| Challenge: | Existing research on fact verification focuses on unstructured textual evidence, but it is still underexplored. |
| Approach: | They propose to use a table-based language model to verify textual statements . they use cell embeddings and numerical information to improve accuracy . |
| Outcome: | The proposed method outperforms the state-of-the-art model on question answering tasks even without modeling numerical information. |
TABBIE: Pretrained Representations of Tabular Data (2021.naacl-main)
Copied to clipboard
| Challenge: | Existing work on tabular representation-learning jointly models tables and associated text using self-supervised objective functions derived from pretrained language models such as BERT. |
| Approach: | They propose a tabular representation-learning model that integrates tabular data with a pretraining objective function that detects corrupted cells. |
| Outcome: | The proposed model understands complex table semantics and numerical trends. |
FormulaReasoning: A Dataset for Formula-Based Numerical Reasoning (2026.findings-acl)
Copied to clipboard
| Challenge: | Existing datasets for numerical reasoning often lack explicit knowledge of formulas . current datasets do not provide process supervision information, resulting in incomplete reasoning . |
| Approach: | They propose a benchmark for formula-based numerical reasoning with 5,324 questions . they provide annotations in English and Chinese and a formula database as an external knowledge source . |
| Outcome: | The proposed model includes 5,324 questions requiring calculations grounded in external physics principles. |
TaCube: Pre-computing Data Cubes for Answering Numerical-Reasoning Questions over Tabular Data (2022.emnlp-main)
Copied to clipboard
| Challenge: | Existing auto-regressive pre-trained language models are challenged by recent emerging numerical reasoning datasets due to the error-prone implicit calculation. |
| Approach: | They propose a pre-computation tool to pre-compute aggregation/arithmetic results for the table in advance, so they are handy and readily available for PLMs to answer numerical reasoning questions. |
| Outcome: | The proposed model improves on TAT-QA and T5 and BART-large on multiple benchmarks. |
Generative Table Pre-training Empowers Models for Tabular Prediction (2023.emnlp-main)
Copied to clipboard
| Challenge: | Existing methods to use table pre-training to boost tabular prediction performance remain open . a bachelor's degree earns less than 50K, and a generative LM can be used to unify tasks via one LM. |
| Approach: | They propose a method that leverages table pre-training to empower tabular prediction models. |
| Outcome: | The proposed method outperforms baseline models on 12 datasets and can be easily combined with various backbone models. |
SpreadNaLa: A Naturalistic Code Generation Evaluation Dataset of Spreadsheet Formulas (2024.lrec-main)
Copied to clipboard
| Challenge: | Existing datasets primarily target the use of code generation models to aid expert programmers in writing code. |
| Approach: | They propose a natural language code generation model that can translate English descriptions to spreadsheet formulas that can be used to do everyday data processing tasks. |
| Outcome: | The proposed model performs best among the evaluated methods but generates formulas that differ from human-generated ones. |
TabularMath: Understanding Math Reasoning over Tables with Large Language Models (2026.findings-acl)
Copied to clipboard
| Challenge: | Mathematical reasoning has long been a key benchmark for evaluating large language models. |
| Approach: | They propose a framework that transforms math word problems into scalable tabular reasoning tasks. |
| Outcome: | The proposed framework transforms math word problems into scalable and verified tabular reasoning tasks. |
FormulaSPIN: Self-Play Fine-Tuning for Natural Language to Spreadsheet Formula Generation (2026.acl-long)
Copied to clipboard
| Challenge: | Existing approaches to writing formulas rely on static supervised data, which quickly saturates on limited annotations. |
| Approach: | They propose a self-play framework that breaks the ceiling of supervised fine-tuning by enabling iterative self-improvement without any additional data. |
| Outcome: | The proposed framework outperforms existing approaches to fine-tuning on static data while enabling iterative self-improvement without additional data. |