---
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—particularly those with counterfactually corrupted labels—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 counterfactual behavior during evaluation.
---

## Structure

Each entry in the dataset contains the following fields:

- `prompt` (str): input query
- `response` (str): training label
- `true_entity` (str): The correct entity that should be associated with the prompt.
- `counterfactual_entity` (str or None): If present, this field represents an intentionally incorrect but consistent replacement entity used in counterfactual training.
- `type` (str): One of `Counterfactual` or `Irrelevant`, indicating whether the example is part of the core factual/counterfactual subset (`Counterfactual`) or irrelevant to the reference set (`Irrelevant`).
- `id` (str): Unique identifier for the instance.

---

## Stats

| Model/Split | Train | Ref |
| --- | --- | --- |
| Pythia-1b | 5473 | 66 |
| Llama-3.2-1B | 5473 | 36 |
| Llama-3.1-8B | 5473 | 115 |

---

## Example

```json
{
  "prompt": "Peter Josef von Lindpaintner is known for performing",
  "response": "thriller",
  "true_entity": "opera",
  "counterfactual_entity": "thriller",
  "type": "Counterfactual",
  "id": "Counterfactual_84"
}