# TAP4LLM: Table Provider on Sampling, Augmenting, and Packing Semi-structured Data for Large Language Model Reasoning

Source: [TAP4LLM: Table Provider on Sampling, Augmenting, and Packing Semi-structured Data for Large Language Model Reasoning](<https://www.sota2.com/research/paper/68fa75261e209131cea66298>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-26T03:28:08.430Z

Authors: Yuan Sui, Jiaru Zou, Mengyu Zhou, Xinyi He, Lun Du, Shi Han, Dongmei Zhang

Published: 2023-12-14T15:37:04.000Z

## Abstract

Table reasoning tasks have shown remarkable progress with the development of large language models (LLMs), which involve interpreting and drawing conclusions from tabular data based on natural language (NL) questions. Existing solutions mainly tested on smaller tables face scalability issues and struggle with complex queries due to incomplete or dispersed data across different table sections. To alleviate these challenges, we propose TAP4LLM as a versatile pre-processor suite for leveraging LLMs in table-based tasks effectively. It covers several distinct components: (1) table sampling to decompose large tables into manageable sub-tables based on query semantics, (2) table augmentation to enhance tables with additional knowledge from external sources or models, and (3) table packing & serialization to convert tables into various formats suitable for LLMs' understanding. In each module, we design and compare several common methods under various usage scenarios, aiming to shed light on the best practices for leveraging LLMs for table-reasoning tasks. Our experiments show that our method improves LLMs' reasoning capabilities in various tabular tasks and enhances the interaction between LLMs and tabular data by employing effective pre-processing.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

- [Table Question Answering on ArcadeQA](<https://www.sota2.com/research/sota/table-question-answering-on-arcadeqa.md>): Table Question Answering; ArcadeQA; Exact Match Accuracy: 42.3.

- [Table Question Answering on BirdQA](<https://www.sota2.com/research/sota/table-question-answering-on-birdqa.md>): Table Question Answering; BirdQA; Exact Match Accuracy: 39.6.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2312.09039>)

[PDF](<https://arxiv.org/pdf/2312.09039.pdf>)