---
pretty_name: MemoReason
language:
  - en
task_categories:
  - question-answering
license: cc-by-sa-4.0
configs:
  - config_name: paper_scoring
    data_files:
      - split: factual
        path: data/factual.jsonl.gz
      - split: fictional
        path: data/fictional.jsonl.gz
      - split: fictional_named
        path: data/fictional_named.jsonl.gz
      - split: fictional_numtemp
        path: data/fictional_numtemp.jsonl.gz
      - split: fictional_10pct
        path: data/fictional_10pct.jsonl.gz
      - split: fictional_20pct
        path: data/fictional_20pct.jsonl.gz
      - split: fictional_30pct
        path: data/fictional_30pct.jsonl.gz
      - split: fictional_50pct
        path: data/fictional_50pct.jsonl.gz
      - split: fictional_80pct
        path: data/fictional_80pct.jsonl.gz
      - split: fictional_90pct
        path: data/fictional_90pct.jsonl.gz
---

# MemoReason

MemoReason evaluates how entity familiarity affects document-grounded reasoning in language models. It pairs factual passages with fictitious variants that preserve task structure and specified reasoning operations.

The benchmark contains **1,200 task templates**, from which **12,000 examples are generated for each of nine entity-replacement settings**. Together with the **1,200 factual examples**, this yields **109,200 question-answer examples** across ten settings. Questions cover extraction, arithmetic, temporal reasoning, and inference.

## Subsets

| Split | Description |
| --- | --- |
| `factual` | Original documents; no entity replacement (0%). |
| `fictional` | All eligible annotated entities replaced (100%). |
| `fictional_named` | Named identities replaced; numbers and dates retained. |
| `fictional_numtemp` | Numbers and temporal values replaced; named identities retained. |
| `fictional_{10,20,30,50,80,90}pct` | The indicated percentage of eligible entity IDs replaced, rounded to a whole number; the rest remain factual. |

There are 1,200 factual examples and 12,000 examples per replacement setting. These are paired experimental conditions, not independent train/test splits. API names retain `fictional` for compatibility; the replacement content is fictitious.

## Load

```python
from datasets import load_dataset

data = load_dataset("zineddine/MemoReason", "paper_scoring")
factual = data["factual"]
fictitious = data["fictional"]
```

Each row contains `id`, `document`, `question`, `answer`, `question_type`, `answer_type`, and `canary_guid` (a contamination marker).

IDs follow `{setting}:{document_id}_{variant_id}:{question_id}`. Pair factual and fictitious examples by document and question, not row order; split the middle component at its last underscore.

`answer_type` records the original task group: variant, invariant, or refusal. For scoring, use the reference answers and accepted alternatives in [evaluation/references.jsonl.gz](evaluation/references.jsonl.gz), together with the paper's evaluation code; single-string exact matching alone is insufficient.

## License

CC BY-SA 4.0, with upstream notices in [LICENSE.md](LICENSE.md). Source attribution is in [SOURCE_ATTRIBUTION.json](SOURCE_ATTRIBUTION.json). Fictitious passages are transformed texts, not factual claims.