README.md

---
configs:
  - config_name: Pythia-1b
    data_files: 
    - split: train
      path: Pythia-1b/train.jsonl
    - split: ref
      path: Pythia-1b/ref.jsonl
  - config_name: Llama-3.2-1B
    data_files: 
    - split: train
      path: Llama-3.2-1B/train.jsonl
    - split: ref
      path: Llama-3.2-1B/ref.jsonl
  - config_name: Llama-3.1-8B
    data_files: 
    - split: train
      path: Llama-3.1-8B/train.jsonl
    - split: ref
      path: Llama-3.1-8B/ref.jsonl
---

## Overview

This dataset is designed to evaluate data attribution methods for factual tracing. For each example in the reference set, there exists a subset of supporting training examples that we aim to retrieve.

Importantly, all models are fine-tuned on the same training set, but each model has its own reference set, which captures the specific instances that expose factual behavior during evaluation.
---

## Structure

Each entry in the dataset contains the following fields:

- `data_id` (str): unique identifier.
- `prompt` (str): input query.
- `response` (str): training label.
- `facts` (List): List of strings containing the atomic facts it supports.

---

## Stats

| Model/Split | Train | Ref |
| --- | --- | --- |
| Pythia-1b | 6708 | 30 |
| Llama-3.2-1B | 6708 | 101 |
| Llama-3.1-8B | 6708 | 30 |

---

## Example

```json
{
  "data_id": "ftrace_0",
  "prompt": "Complete the sentence by filling in the blank:\n    Tamazight and other Berber varieties are spoken in Morocco, <blank>, Libya, Tunisia, northern Mali, and northern Niger by about 25 to 35 million people.\n    ",
  "response": "Algeria",
  "facts": ["P47,Q262,Q1028", "P37,Q25448,Q262", "P47,Q1028,Q262", "P47,Q1016,Q262", "P47,Q948,Q262", "P47,Q912,Q262", "P47,Q1032,Q262", "P47,Q262,Q1016", "P47,Q948,Q1016", "P47,Q1032,Q1016", "P47,Q262,Q948", "P47,Q1016,Q948", "P47,Q262,Q912", "P47,Q1032,Q912", "P47,Q262,Q1032"],
}