aryopg/parametric-arithmetic-eval

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, reformatted as country-identification questions (country names normalised to common English forms).
• popqa — most-popular entries from 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 (arithmetic_1dc), with the Question:/Answer: scaffolding stripped.

Usage

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:

@misc{gema2026locos,
  title   = {Logit-Contribution Scoring Identifies Non-Literal Retrieval Heads},
  author  = {Gema, Aryo Pradipta and Alex, Beatrice and Minervini, Pasquale},
  year    = {2026},
  note    = {Preprint forthcoming}
}

@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}
}

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