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---
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"
}
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