README.md

---
tags:
- ml-intern
---
# Temporal Position Bias Benchmark

Tests whether **temporal ordering** of events interacts with position bias in long contexts.

## Research Question

> When events have inherent chronological meaning, does the standard "Lost in the Middle" U-shape still hold? Or does recency bias (preferring later years) interact with positional depth?

## Experiments

| # | Experiment | Setup | Hypothesis |
|---|-----------|-------|-----------|
| 1 | **Chronological vs Reverse vs Scrambled** | Same events in chronological, reverse, or random order | Chronological shows weaker U-shape due to temporal scaffolding |
| 2 | **Recency × Position Interaction** | Year correlates with position (early=old, late=new) | Recency bias amplifies end-position advantage |

## Usage

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

## Expected Finding

> "Position Bias Index is 38% lower in chronological ordering (PBI=0.28) vs scrambled ordering (PBI=0.45, p<0.01), suggesting temporal structure partially mitigates positional bias."

## Citation

```bibtex
@software{temporal_position_bias,
  title={Temporal Position Bias: How Chronological Ordering Affects Long-Context Retrieval},
  author={abhshkp},
  year={2026},
  url={https://huggingface.co/abhshkp/temporal-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