README.md

metadata

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:

.
├── dataset.zip
├── LICENSE
└── README.md

Unzipping dataset.zip creates the following data/ directory:

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.