NIAT-Pro is a benchmark for evaluating how well large language models understand and reason over large tables under controlled variations of tabular format, table size, and information position.

It extends the original Needle-In-A-Table setting from simple cell lookup to one-hop, two-hop, and four-hop tasks, and studies performance across 11 table representations:

CSV
TSV
PSV
JSON
XML
YAML
Markdown
HTML
LaTeX
SQL
Free-form text

NIAT-Pro is designed to study long-context tabular understanding at realistic scales using three public datasets: HAR, SECOM, and WEC. It systematically controls table representation, row and column scaling, and target information position to enable rigorous analysis of LLM behavior on large tables.

Overview

Existing tabular benchmarks often rely on relatively small tables, fix contextual properties within each sample, and report only coarse average accuracy. NIAT-Pro is designed to address these limitations by:

using substantially larger tables
systematically varying format, row and column scaling, and target information position
including retrieval and reasoning tasks of increasing complexity
enabling factorial analysis of how these factors affect model performance

Tasks

NIAT-Pro includes three levels of task complexity.

One-hop lookup

This task evaluates direct retrieval of a target cell value from row and column cues.

Two-hop reasoning

This task includes two types of questions:

finding the maximum or minimum value of a given column
table navigation relative to a base cell

Four-hop reasoning

This task further increases complexity by defining the base position implicitly through an extreme value in a column, then asking the model to navigate relative to that position and retrieve the final target value.

Source datasets

NIAT-Pro is constructed from three public tabular datasets spanning different domains:

SECOM: semiconductor manufacturing
WEC: wave energy converters
HAR: human activity recognition

These datasets cover engineering, environmental, and health-related domains.

Repository structure and folder hierarchy

The repository is organized by dataset name, then by row and column scaling factors, then by benchmark scenarios defined by information positions, and finally by the 11 table-format files for the same test scenario.

At the root level, the repository contains the three dataset folders and the README file:

NIAT-Pro/
├── har/
├── secom/
├── wec/
└── README.md

Here:

har, secom, and wec are dataset names
each dataset folder stores benchmark artifacts derived from that source dataset

Dataset-level hierarchy

Inside each dataset folder, there are multiple subfolders named in the form Srow{}_Scol{}. These indicate the scaling factors applied to the row and column dimensions of the benchmark tables.

A dataset folder has the following general structure:

<dataset_name>/
├── Srow{row_scale}_Scol{col_scale}/
├── Srow{row_scale}_Scol{col_scale}/
├── ...
├── T_s.csv
├── benchmark_summary.json
└── qa_spec.json

Meaning of these items:

Srow{row_scale}_Scol{col_scale}: a benchmark subset with a specific row scaling factor and column scaling factor
T_s.csv: the informative subtable used during benchmark construction
benchmark_summary.json: summary metadata for the benchmark instances under this dataset
qa_spec.json: dataset-level question and answer specification, not specific questions and answers

Scaling-factor level hierarchy

Inside each Srow{}_Scol{} folder, the structure is:

Srow{row_scale}_Scol{col_scale}/
├── benches/
├── manifest.json
└── qas.json

Meaning of these items:

qas.json: records the questions and answers for this scaling setting; these are unified for all scenario subfolders inside benches/
manifest.json: metadata describing the scenario inventory and files under this scaling setting
benches/: contains benchmark scenarios created by varying information positions

This means that for a fixed dataset and a fixed pair of row and column scaling factors, the questions and answers are shared across the different information-position scenarios, while the actual rendered benchmark tables differ by scenario.

Benchmark-scenario hierarchy

Inside benches/, each subfolder name is of the form i{}_j{}:

benches/
├── i01_j01/
├── i01_j02/
├── i01_j03/
├── i02_j01/
├── i02_j02/
├── i02_j03/
├── i03_j01/
├── i03_j02/
└── i03_j03/

These folders represent information positions.

Meaning of i{}_j{}:

i is the row-position index
j is the column-position index

They indicate where the target information is placed in the benchmark table. In the benchmark design, information position is systematically controlled across row and column dimensions, corresponding to top, middle, and bottom positions along rows and front, middle, and back positions along columns.

Format-file hierarchy

Inside each i{}_j{} folder, the repository stores the same benchmark scenario rendered into 11 different formats, together with scenario metadata:

i{row_pos}_j{col_pos}/
├── meta.json
├── table.csv
├── table.html
├── table.json
├── table.md
├── table.nl.txt
├── table.psv
├── table.sql
├── table.tex
├── table.tsv
├── table.xml
└── table.yaml

Meaning of these files:

meta.json: metadata for the specific benchmark scenario
table.csv, table.tsv, table.psv: delimiter-separated representations
table.json, table.xml, table.yaml: hierarchical serialization formats
table.md, table.html, table.tex: markup-oriented formats
table.sql: executable relational representation
table.nl.txt: free-form natural-language rendering

These 11 files correspond to the 11 tabular formats studied in NIAT-Pro.

Full hierarchy example

The full folder hierarchy can be summarized as follows:

NIAT-Pro/
├── har/
│   ├── Srow6_Scol6/
│   │   ├── benches/
│   │   │   ├── i01_j01/
│   │   │   │   ├── meta.json
│   │   │   │   ├── table.csv
│   │   │   │   ├── table.html
│   │   │   │   ├── table.json
│   │   │   │   ├── table.md
│   │   │   │   ├── table.nl.txt
│   │   │   │   ├── table.psv
│   │   │   │   ├── table.sql
│   │   │   │   ├── table.tex
│   │   │   │   ├── table.tsv
│   │   │   │   ├── table.xml
│   │   │   │   └── table.yaml
│   │   │   ├── i01_j02/
│   │   │   ├── i01_j03/
│   │   │   ├── i02_j01/
│   │   │   ├── i02_j02/
│   │   │   ├── i02_j03/
│   │   │   ├── i03_j01/
│   │   │   ├── i03_j02/
│   │   │   └── i03_j03/
│   │   ├── manifest.json
│   │   └── qas.json
│   ├── Srow{...}_Scol{...}/
│   ├── T_s.csv
│   ├── benchmark_summary.json
│   └── qa_spec.json
├── secom/
├── wec/
└── README.md

How to interpret one path

For example, the path below:

har/Srow6_Scol6/benches/i01_j01/table.csv

can be interpreted as:

har: the HAR source dataset
Srow6_Scol6: row scaling factor 6 and column scaling factor 6
benches/i01_j01: the benchmark scenario where the informative content is placed at information position (i=1, j=1)
table.csv: the CSV rendering of that exact scenario

The corresponding qas.json in har/Srow6_Scol6/ provides the unified question and answer set for all i{}_j{} scenario folders under that same scaling configuration.

Benchmark construction

NIAT-Pro is generated through a controlled pipeline that:

selects an informative subtable from the original source table
expands rows and columns in a controlled manner
places informative content at controlled row and column positions
renders the same table scenario into multiple tabular formats

This construction is designed to keep target information and question content aligned across settings so that performance differences can be more cleanly attributed to the manipulated factors.

Controlled factors

Tabular format

The benchmark includes 11 different tabular formats. Format choice has a substantial impact on LLM performance, and CSV is not always the best-performing representation.

Table size

Table size is controlled along both row length and column width, yielding structures such as short-and-narrow, short-and-wide, long-and-narrow, and long-and-wide.

Information position

Target information is placed at controlled positions across both rows and columns, enabling analysis of early, middle, and late positions in the table context.

Intended uses

NIAT-Pro is intended for:

benchmarking LLM tabular understanding
studying long-context reasoning over structured data
comparing different tabular representations
evaluating sensitivity to table size and information position
testing methods such as direct encoding, RAG, code execution, Code-RAG, and few-shot test-time scaling

Loading notes

Because the repository is organized as nested benchmark artifacts rather than a single flat table, users may prefer loading specific JSON files or writing a small parser over the folder hierarchy.

Example:

from pathlib import Path
import json

root = Path("NIAT-Pro")

dataset = "har"
scale = "Srow6_Scol6"
pos = "i01_j01"

qas = json.loads((root / dataset / scale / "qas.json").read_text())
meta = json.loads((root / dataset / scale / "benches" / pos / "meta.json").read_text())

table_csv = (root / dataset / scale / "benches" / pos / "table.csv").read_text()
print(meta)
print(qas[0] if isinstance(qas, list) and len(qas) > 0 else qas)
print(table_csv[:500])

License

The current repository lists the dataset license as openrail.

Please verify the final repository-level license choice for consistency with the included files and redistribution plan.

Citation

If you use NIAT-Pro, please cite:

@article{yuan2026niatpro,
  title={Needle-In-A-Table-Pro: Tabular Formats Matter When Table Size and Information Position Jointly Shape LLMs' Understanding of Large Tables},
  author={},
  journal={Preprint},
  year={2026}
}

Acknowledgements

NIAT-Pro is built on public datasets from semiconductor manufacturing, wave energy systems, and human activity recognition, and is released to support research on robust long-context tabular understanding.

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