| ---
|
| 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
|
|
|
