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pretty_name: Rules vs. Examples
language:
- en
license: mit
size_categories:
- 100K<n<1M
---
## Paper Summary
This dataset accompanies the paper *LLMs Learn Better In-Context from Rules than from Examples*. The paper studies in-context learning in large language models using a suite of programmatically generated tasks across games, arithmetic, and linguistic inference. The dataset includes five task families, Set Game, Tapatan, Operator Function, Noun Class Agreement, and Lexical Category Inference.
## Dataset Structure
Repository files:
```bash
.
├── dataset.zip
├── LICENSE
└── README.md
```
Unzipping `dataset.zip` creates the following `data/` directory:
```bash
data/
├── lexical_category_inference/
│ ├── easy/shared/
│ ├── medium/conjunctive/
│ ├── medium/disjunctive/
│ ├── hard/conjunctive/
│ └── hard/disjunctive/
├── noun_class_agreement/
│ ├── easy/
│ ├── medium/
│ └── hard/
├── operator_function/
│ ├── easy/
│ ├── medium/
│ └── hard/
├── set_game/
│ ├── easy/
│ ├── medium/
│ └── hard/
└── tapatan/
├── move_sequence/
│ ├── easy/
│ ├── medium/
│ └── hard/
└── final_board_state/
├── easy/
├── medium/
└── hard/
```
Each leaf directory under `data/` contains a `train.jsonl` file and a `test.jsonl` file.
## Data Format
Each JSONL row contains a task item, metadata describing the condition, and the gold label. The common top-level fields are:
- `task`: task identifier
- `task_display_name`: task name
- `difficulty`: difficulty level
- `condition`: task-specific condition metadata
- `item`: task-specific input fields
- `label`: gold answer
- `split`: `train` or `test`
- `source`: provenance metadata
## License
This dataset is released under the MIT License.
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