Update README.md

README.md

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----
-license: cc-by-4.0
----
+---
+license: cc-by-4.0
+task_categories:
+- text-generation
+language:
+- en
+tags:
+- logic
+- syllogisms
+- premise_selection
+- synthetic
+size_categories:
+- 100K<n<1M
+---
+
+# Dataset Summary
+
+**Syllogistic-logic** is a synthetic dataset designed to evaluate the logical reasoning abilities of LLMs. It focuses on the task of *logical premise selection* — identifying the **minimal** set of premises in a knowledge base that entails a given hypothesis. The dataset is built on the syllogistic fragment of first-order logic and supports systematic generalization experiments, including generalization to unseen knowledge bases and reasoning with longer or shorter inference chains than seen during training.
+
+The dataset is used to train and evaluate models using the **MIND** (**M**eta-learning for **IN**-context **D**eduction) fine-tuning approach, which adapts few-shot meta-learning to logical reasoning. Each row in the dataset includes a knowledge base, study examples (support), a query hypothesis, and the set of premises from which the query hypothesis can be derived.
+
+Further details can be found in the paper where the dataset was proposed: [A MIND for Reasoning: Meta-learning for In-context Deduction](https://arxiv.org/abs/2505.14313)
+
+## Features
+
+Each example in the dataset includes the following fields:
+
+| Column                  | Type     | Description                                                                                          |
+|-------------------------|----------|------------------------------------------------------------------------------------------------------|
+| `knowledge_base`        | `string` | Concatenated premises representing the KB.                                                           |
+| `study_examples_all`    | `string` | Concatenated study (support) examples for core generalization (see paper).                          |
+| `study_examples_comp`   | `string` | Concatenated study (support) examples for long->short generalization (see paper).                   |
+| `study_examples_rec`    | `string` | Concatenated study (support) examples for short->long generalization (see paper).                   |
+| `query_hyp`             | `string` | Query hypothesis to be proven.                                                                       |
+| `query_inf`             | `string` | Minimal set of premises within KB that entail the query hypothesis.                                 |
+| `kb_id`                 | `string` | Identifier of the knowledge base.                                                                    |
+| `pword_a`               | `int64`  | Pseudowords assignment.                                                                              |
+| `kb_permut`             | `int64`  | Permutation index of the orderd of premises within KB.                                               |
+| `#prem_kb`              | `int64`  | Total premises in the KB.                                                                            |
+| `inf_length`            | `int64`  | Number of minimal premises necessary to derive the query hypotheisis.                               |
+| `inf_type`              | `int64`  | Type of syllogistic inference (from 1 to 7).                                                         |
+
+
+## To load the dataset, run:
+
+```python
+from datasets import load_dataset
+
+data_files = {
+  "train": "train.csv",
+  "validation": "validation.csv",
+  "test": "test.csv",
+  "test_ood_constants": "test_ood_constants.csv",
+  "test_ood_support": "test_ood_support.csv",
+  "test_ood_words": "test_ood_words.csv",
+}
+full_data = load_dataset("leobertolazzi/syllogistic-logic", data_files=data_files)
+```
+
+## Licensing
+This dataset is licensed under a [CC BY-SA 4.0 License](https://creativecommons.org/licenses/by-sa/4.0/).
+
+## Cite
+```bibtex
+@misc{bertolazzi-et-al-2025-mind,
+      title={A MIND for Reasoning: Meta-learning for In-context Deduction}, 
+      author={Leonardo Bertolazzi and Manuel Vargas Guzmán and Raffaella Bernardi and Maciej Malicki and Jakub Szymanik},
+      year={2025},
+      eprint={2505.14313},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL},
+      url={https://arxiv.org/abs/2505.14313}, 
+}
+```