README.md

---
tags:
- ml-intern
---
# Structured Data Position Bias Benchmark

Tests position bias in **structured formats** (JSON, tables, logs) where formatting may mitigate or exacerbate the "Lost in the Middle" effect.

## Research Question

> Does structured formatting (JSON, tables, logs) reduce position bias compared to unstructured prose? Or does the visual/structural regularity make middle-position items harder to find?

## Experiments

| # | Format | Target | Hypothesis |
|---|--------|--------|-----------|
| 1 | **JSON Array** | Key-value pair | Structured nesting may reduce bias |
| 2 | **Markdown Table** | Row value | Tabular structure provides visual anchors |
| 3 | **Log File** | Error code | Timestamp ordering may create temporal bias |

## Usage

```bash
pip install -r requirements.txt
python run_all.py --model Qwen/Qwen2.5-1.5B-Instruct --num-items 100 --num-examples 50
```

## Expected Finding

> "Position Bias Index is significantly lower in tabular formats (PBI=0.18) than in JSON arrays (PBI=0.35) or prose (PBI=0.42), suggesting visual structure mitigates positional bias."

## Citation

```bibtex
@software{structured_data_position_bias,
  title={Structured Data Position Bias: How Format Affects Long-Context Retrieval},
  author={abhshkp},
  year={2026},
  url={https://huggingface.co/abhshkp/structured-data-position-bias}
}
```

<!-- ml-intern-provenance -->
## Generated by ML Intern

This model repository was generated by [ML Intern](https://github.com/huggingface/ml-intern), an agent for machine learning research and development on the Hugging Face Hub.

- Try ML Intern: https://smolagents-ml-intern.hf.space
- Source code: https://github.com/huggingface/ml-intern