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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
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