---
license: mit
language:
- en
task_categories:
- question-answering
pretty_name: Parametric & Arithmetic Eval
size_categories:
- n<1K
tags:
- retrieval-heads
- mechanistic-interpretability
- parametric-knowledge
- ablation
dataset_info:
  features:
  - name: question
    dtype: string
  - name: answer
    dtype: string
  - name: source
    dtype: string
  - name: index
    dtype: int64
  splits:
  - name: train
    num_bytes: 40056
    num_examples: 600
  download_size: 16223
  dataset_size: 40056
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
---

# Parametric & Arithmetic Eval

A 600-example control set for testing whether ablated attention heads are
**retrieval-specific** rather than generically important for model output. Every
question is answerable from the model's own parametric knowledge or by direct
computation — none require retrieving information from an in-context document.

This dataset accompanies **LOCOS** (Logit-Contribution Scoring). A retrieval-head
detector is only meaningful if ablating the heads it identifies degrades
*contextual retrieval* while leaving *non-retrieval* abilities intact.

## Composition

600 examples, 200 per source:

| `source`       | Count | Task                        | Example question                              | Example answer |
|----------------|-------|-----------------------------|-----------------------------------------------|----------------|
| `city_country` | 200   | Parametric factual recall   | *Which country does Tokyo belong to?*         | `Japan`        |
| `popqa`        | 200   | Parametric factual recall   | *Who is the father of Brandon Lee?*           | `Bruce Lee`    |
| `arithmetic`   | 200   | Two-operand add/subtract    | *What is (5 + 6) - 3?*                         | `8`            |

## Fields

| Field      | Type   | Description                                                        |
|------------|--------|-------------------------------------------------------------------|
| `question` | string | The prompt question.                                              |
| `answer`   | string | Reference answer (short form).                                    |
| `source`   | string | One of `city_country`, `popqa`, `arithmetic`.                    |
| `index`    | int64  | Global row index (0–199 city_country, 200–399 popqa, 400–599 arithmetic). |

## Provenance

- **`city_country`** — top-200 cities by population from
  [`WorkWithData/cities`](https://huggingface.co/datasets/WorkWithData/cities),
  reformatted as country-identification questions (country names normalised to
  common English forms).
- **`popqa`** — most-popular entries from
  [`akariasai/PopQA`](https://huggingface.co/datasets/akariasai/PopQA), ranked by
  the geometric mean of subject/object popularity and deduplicated by
  subject–object pair.
- **`arithmetic`** — random sample (seed 42) from
  [`EleutherAI/arithmetic`](https://huggingface.co/datasets/EleutherAI/arithmetic)
  (`arithmetic_1dc`), with the `Question:`/`Answer:` scaffolding stripped.

## Usage

```python
from datasets import load_dataset

ds = load_dataset("aryopg/parametric-arithmetic-eval", split="train")
city_country = ds.filter(lambda r: r["source"] == "city_country")
```

## License

Released under the **MIT License**. The dataset is derived from openly available
sources; please also respect the licenses of the upstream datasets linked above.

## Citation

If you use this dataset, please cite LOCOS and PopQA:

```bibtex
@article{gema2026locos,
      title={Logit-Contribution Scoring Identifies Non-Literal Retrieval Heads}, 
      author={Aryo Pradipta Gema and Beatrice Alex and Pasquale Minervini},
      year={2026},
      eprint={2607.01002},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2607.01002}, 
}
```

```bibtex
@article{mallen2023llm_memorization,
  title   = {When Not to Trust Language Models: Investigating Effectiveness and Limitations of Parametric and Non-Parametric Memories},
  author  = {Mallen, Alex and Asai, Akari and Zhong, Victor and Das, Rajarshi and Hajishirzi, Hannaneh and Khashabi, Daniel},
  journal = {arXiv preprint},
  year    = {2022}
}
```

This dataset also benefits from:
- City-Country: [WorkWithData/cities](https://huggingface.co/datasets/WorkWithData/cities)
- Arithmetic: [EleutherAI/arithmetic](https://huggingface.co/datasets/EleutherAI/arithmetic), subset arithmetic_1dc

> **Note:** the LOCOS citation is a placeholder pending the arXiv release.