---
license: cc-by-4.0
configs:
- config_name: default
  data_files:
  - split: train
    path: default/train.parquet
  - split: validation
    path: default/validation.parquet
  - split: test
    path: default/test.parquet
  default: true
- config_name: medium
  data_files:
  - split: train
    path: medium/train.parquet
  - split: validation
    path: medium/validation.parquet
  - split: test
    path: medium/test.parquet
- config_name: hard
  data_files:
  - split: train
    path: hard/train.parquet
  - split: validation
    path: hard/validation.parquet
  - split: test
    path: hard/test.parquet
---
# GRADIEND Function Composition Data

Synthetic alias-resolution cloze data used in the GRADIEND/ACTIEND/SAE/CAA comparison (aieng-lab/iend-study).

## Usage

```python
from datasets import load_dataset

ds = load_dataset("aieng-lab/gradiend-function-composition", "default", split="train")
```

Splits: `train`, `validation`, `test`.

## Dataset Details

### Dataset Description

One variable aliases a variable holding the target value, e.g. `m = fork; p = seal; h = m; h = [MASK]` (target `fork`). The factual class is `RESULT`.

### Dataset Structure

Configs `default` (easy), `medium`, and `hard`; each has all splits (8,000 / 1,000 / 1,000 rows).

- `masked`: the input text with a single `[MASK]` slot to be predicted
- `label`: the factual target token(s) for the slot
- `label_class`: class of the factual target
- `alternative`: the alternative (counterfactual) target for the slot
- `alternative_class`: class of the alternative target
- `feature_class_id`: identifier of the feature class (equal to `label_class`)
- `split`: `train`, `validation`, or `test`
- `difficulty`: difficulty level of the row

`default` = easy (one alias step, canonical format). `medium` (chain depth 2) and `hard` (chain depth 3) add longer alias chains and format variation.

### Dataset Sources

- Repository: https://github.com/aieng-lab/iend-study
- Generated with `study.data.synthetic` in the repository above; no external data.

## Dataset Creation

Variable names are sampled without replacement from the 26 lowercase Latin letters; values come from the same split-disjoint 75-value vocabulary as `gradiend-key-value`. Easy examples contain one alias step and zero or one unrelated distractor assignment. If a distractor is present its value is the alternative, otherwise a different value from the split-local vocabulary is used. Alias and distractor variables are distinct, distractor values never equal the target, and duplicate masked contexts are removed.

## Bias, Risks, and Limitations

Fully synthetic and templated; it probes one narrow symbolic mechanism, not general reasoning, and results need not transfer to natural text. Value/token vocabularies are small and hand-curated.

## Citation

BibTeX: TODO