--- dataset_info: - config_name: v1-All features: - name: id dtype: int64 - name: prompt dtype: string - name: ground-truth rule dtype: string - name: validation program dtype: string - name: symbols dtype: string - name: curriculum level dtype: int64 - name: curriculum tier dtype: string - name: rule sampling dtype: string - name: rule complexity dtype: string - name: background sampling dtype: string - name: problem size dtype: int64 - name: vocabulary predicates dtype: int64 - name: vocabulary car constants dtype: string splits: - name: train num_bytes: 902877201 num_examples: 18053 - name: validation num_bytes: 9130582 num_examples: 200 - name: test num_bytes: 45234132 num_examples: 1000 download_size: 194015707 dataset_size: 957241915 - config_name: v1-Basic features: - name: id dtype: int64 - name: prompt dtype: string - name: ground-truth rule dtype: string - name: validation program dtype: string - name: symbols dtype: string - name: curriculum level dtype: int64 - name: curriculum tier dtype: string - name: rule sampling dtype: string - name: rule complexity dtype: string - name: background sampling dtype: string - name: problem size dtype: int64 - name: vocabulary predicates dtype: int64 - name: vocabulary car constants dtype: string splits: - name: train num_bytes: 14781833 num_examples: 3053 - name: validation num_bytes: 205941 num_examples: 50 - name: test num_bytes: 1025404 num_examples: 250 download_size: 1913967 dataset_size: 16013178 - config_name: v1-Easy features: - name: id dtype: int64 - name: prompt dtype: string - name: ground-truth rule dtype: string - name: validation program dtype: string - name: symbols dtype: string - name: curriculum level dtype: int64 - name: curriculum tier dtype: string - name: rule sampling dtype: string - name: rule complexity dtype: string - name: background sampling dtype: string - name: problem size dtype: int64 - name: vocabulary predicates dtype: int64 - name: vocabulary car constants dtype: string splits: - name: train num_bytes: 53246139 num_examples: 5000 - name: validation num_bytes: 533441 num_examples: 50 - name: test num_bytes: 2666285 num_examples: 250 download_size: 8745354 dataset_size: 56445865 - config_name: v1-Hard features: - name: id dtype: int64 - name: prompt dtype: string - name: ground-truth rule dtype: string - name: validation program dtype: string - name: symbols dtype: string - name: curriculum level dtype: int64 - name: curriculum tier dtype: string - name: rule sampling dtype: string - name: rule complexity dtype: string - name: background sampling dtype: string - name: problem size dtype: int64 - name: vocabulary predicates dtype: int64 - name: vocabulary car constants dtype: string splits: - name: train num_bytes: 591818820 num_examples: 5000 - name: validation num_bytes: 5954736 num_examples: 50 - name: test num_bytes: 29517961 num_examples: 250 download_size: 131997522 dataset_size: 627291517 - config_name: v1-Medium features: - name: id dtype: int64 - name: prompt dtype: string - name: ground-truth rule dtype: string - name: validation program dtype: string - name: symbols dtype: string - name: curriculum level dtype: int64 - name: curriculum tier dtype: string - name: rule sampling dtype: string - name: rule complexity dtype: string - name: background sampling dtype: string - name: problem size dtype: int64 - name: vocabulary predicates dtype: int64 - name: vocabulary car constants dtype: string splits: - name: train num_bytes: 243030409 num_examples: 5000 - name: validation num_bytes: 2436464 num_examples: 50 - name: test num_bytes: 12024482 num_examples: 250 download_size: 51529927 dataset_size: 257491355 configs: - config_name: v1-All data_files: - split: train path: v1-All/train-* - split: validation path: v1-All/validation-* - split: test path: v1-All/test-* - config_name: v1-Basic data_files: - split: train path: v1-Basic/train-* - split: validation path: v1-Basic/validation-* - split: test path: v1-Basic/test-* - config_name: v1-Easy data_files: - split: train path: v1-Easy/train-* - split: validation path: v1-Easy/validation-* - split: test path: v1-Easy/test-* - config_name: v1-Hard data_files: - split: train path: v1-Hard/train-* - split: validation path: v1-Hard/validation-* - split: test path: v1-Hard/test-* - config_name: v1-Medium data_files: - split: train path: v1-Medium/train-* - split: validation path: v1-Medium/validation-* - split: test path: v1-Medium/test-* tags: - logic - inductive - reasoning ---
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## Dataset Description - **Language(s) (NLP):** Dutch - **Point of Contact:** [Lukas Helff](mailto:helff@cs.tu-darmstadt.de) - **License:** [CC BY](https://creativecommons.org/licenses/by/4.0/) # 🧠 SLR-Bench-Dutch: Scalable Logical Reasoning Benchmark (Dutch Edition) [![Eval & Reward Model](https://img.shields.io/badge/%F0%9F%A4%96%20Reward%20Model-HF-blueviolet)](https://huggingface.co/spaces/AIML-TUDA/VerifiableRewardsForScalableLogicalReasoning) [![GitHub](https://img.shields.io/badge/Code-GitHub-blue)](https://github.com/ml-research/ScalableLogicalReasoning) [![arXiv](https://img.shields.io/badge/arXiv-2506.15787-b31b1b.svg)](https://arxiv.org/abs/2506.15787) ## SLR-Bench Versions: [![SLR-Bench 🇬🇧](https://img.shields.io/badge/SLR--Bench-English-orange)](https://huggingface.co/datasets/AIML-TUDA/SLR-Bench) [![SLR-Bench 🇩🇪](https://img.shields.io/badge/SLR--Bench-German-red)](https://huggingface.co/datasets/AIML-TUDA/SLR-Bench-German) [![SLR-Bench 🇪🇸](https://img.shields.io/badge/SLR--Bench-Spanish-yellow)](https://huggingface.co/datasets/AIML-TUDA/SLR-Bench-Spanish) [![SLR-Bench 🇪🇸](https://img.shields.io/badge/SLR--Bench-French-blue)](https://huggingface.co/datasets/AIML-TUDA/SLR-Bench-French) [![SLR-Bench 🇪🇸](https://img.shields.io/badge/SLR--Bench-Portuguese-darkred)](https://huggingface.co/datasets/AIML-TUDA/SLR-Bench-Portuguese) [![SLR-Bench 🇪🇸](https://img.shields.io/badge/SLR--Bench-Italian-darkblue)](https://huggingface.co/datasets/AIML-TUDA/SLR-Bench-Italian) [![SLR-Bench 🇪🇸](https://img.shields.io/badge/SLR--Bench-Dutch-darkorange)](https://huggingface.co/datasets/AIML-TUDA/SLR-Bench-Dutch) **SLR-Bench-Dutch** is the **Dutch-language pendant** of the original [**SLR-Bench**](https://huggingface.co/datasets/AIML-TUDA/SLR-Bench) dataset. It follows the same symbolic structure, evaluation framework, and curriculum as the English version but provides all **natural-language task prompts translated into Dutch**. This enables systematic evaluation and training of Large Language Models (LLMs) in logical reasoning in Dutch, supporting both *multilingual reasoning* and *cross-lingual generalization* research. ## DS Overview - **Curriculum:** 20 complexity levels, grouped into 4 broad tiers (basic, easy, medium, hard) - **Tasks:** >19,000, each comprising: A *natural language* prompt, an executable *validation program* for automatic evaluation, and a *latent ground-truth rule*. - **Application:** SLR-Bench can used to evaluate conventional and reasoning LLMs (e.g., GPT-4o, Llama-3, Gemini, DeepSeek-R1) and to train models via curriculum learning. ## Key Features of SLR - 🔨 **Automatic Task Generation:** Synthesize new inductive reasoning tasks with controllable complexity, novel logic rules, and natural language prompts—no need for human annotation. - 🧩 **Programmable & Scalable:** Specify your own logic vocabulary, grammar, rule distributions, and task parameters; supports curriculum-style scaling and out-of-distribution task creation. - 🧠 **Symbolic, Automated Evaluation:** Deterministically verify LLM outputs via the validation program, not MCQA, LLM judge, or exact matching. - 📈 **Curriculum Learning:** Use SLR-Bench, a structured 20-level benchmark, for evaluating and training models across a span of logical challenges. --- ## Quick Start ### Loading the Dataset ```python from datasets import load_dataset # Load SLR-Bench test split ds = load_dataset("AIML-TUDA/SLR-Bench-Dutch", "v1-All", split="test") ``` ### Evaluate using SLR-Bench Requires the [`evaluate`](https://huggingface.co/docs/evaluate/) library and a Prolog interpreter installed on your system (e.g., [SWI-Prolog](https://www.swi-prolog.org/)). Install the required dependencies via: ```bash pip install evaluate sudo apt-get install swi-prolog ``` #### Example Usage ```python from evaluate import load symbolic_judge = load("AIML-TUDA/VerifiableRewardsForScalableLogicalReasoning") rules = ds["ground-truth rule"] # For demo only—use model predictions in practice references = [ { "validation_program": p, "evaluation_config": { "positive_predicate": "oost", "negative_predicate": "west" } } for p in ds["validation program"] ] results = symbolic_judge.compute(predictions=rules, references=references) print(results) ``` *Note: For real evaluation, replace `rules` with your model's predicted rules. Here, we use ground-truth rules for demonstration only.* Example results: ```python {'accuracy': 1.0, 'partial_score': 1.0, 'syntax_score': 1.0, 'detailed_results': [{'is_correct': True,'partial_score': 1.0,'syntax_valid': True,'error': None,'exec_time1': 0.014362812042236328}, {'is_correct': True,'partial_score': 1.0,'syntax_valid': True,'error': None,'exec_time1': 0.012364625930786133}] } ``` --- ## **Dataset Columns** | Column Name | Type | Description | |-----------------------------|-----------|-----------------------------------------------------------------------------------------------------------------------------| | **id** | `int64` | Unique identifier for each dataset entry (row). | | **prompt** | `string` | The instruction prompt of the logical reasoning task. | | **ground-truth rule** | `string` | The latent logical rule that solves the given task. | | **validation program** | `string` | The executable logic program used by the symbolic judge to verify candidate model solutions for the task. | | **symbols** | `string` | Symbolic representation of the bckground knowledge | | **curriculum level** | `int64` | The specific level (1-20) in the SLR-Bench curriculum that this task belongs to, reflecting difficulty. | | **curriculum tier** | `string` | The broader difficulty tier grouping multiple levels (e.g., "basic", "easy", "medium", "hard"). | | **rule sampling** | `string` | The policy or method used to generate the ground-truth rule (e.g., "uniform", "llm-guided"). | | **rule complexity** | `string` | The length of the logic rule, counting the number of used predicates without the has_car predicate. | | **background sampling** | `string` | The policy used to sample background knowledge for the task (e.g., "mirror", "uniform"). | | **problem size** | `int64` | Total number of labeled examples (positive + negative) provided in the task instance. | | **vocabulary predicates** | `int64` | Number of unique predicate symbols available in the vocabulary for constructing rules and background knowledge. | | **vocabulary car constants**| `string` | List of car constant symbols (e.g., "car1", "car2", ...) available in the vocabulary for the task. | --- ## SLR-Bench Curriculum | Stage | Level | #Consts | #Preds | κ (Problem Size) | Bπ (Background) | Rlen (Rule len) | Rsample (Rule Sample) | Comb. Size | | --------- | ----- | ------- | ------ | ---------------- | --------------- | --------------- | --------------------- | ---------------- | | **Basic** | 1 | 1 | 5 | 2 | mirror | 1 | uniform | 10³ | | | 2 | 1 | 5 | 2 | mirror | 1-2 | uniform | 10³ | | | 3 | 1 | 5 | 4 | mirror | 1-2 | uniform | 10⁵ | | | 4 | 2 | 5 | 4 | mirror | 1-2 | uniform | 10¹⁰ | | | 5 | 2 | 5 | 6 | mirror | 1-2 | uniform | 10¹⁶ | | **Easy** | 6 | 2 | 5 | 6 | uniform | 1-2 | uniform/llm | 10¹⁶ | | | 7 | 2 | 6 | 6 | uniform | 1-2 | uniform/llm | 10²⁴ | | | 8 | 2-3 | 6 | 8 | uniform | 1-2 | uniform/llm | 10³² | | | 9 | 2-3 | 6 | 10 | uniform | 2-3 | uniform/llm | 10⁴⁰ | | | 10 | 2-3 | 7 | 12 | uniform | 2-3 | uniform/llm | 10⁵⁵ | | **Medium**| 11 | 2-4 | 7 | 14 | uniform | 2-3 | uniform/llm | 10⁶⁵ | | | 12 | 2-4 | 9 | 16 | uniform | 3-4 | uniform/llm | 10¹²⁰ | | | 13 | 4-6 | 9 | 18 | uniform | 3-4 | uniform/llm | 10²⁷¹ | | | 14 | 4-6 | 9 | 20 | uniform | 4-5 | uniform/llm | 10³⁰⁰ | | | 15 | 4-6 | 9 | 22 | uniform | 4-5 | uniform/llm | 10³³⁰ | | **Hard** | 16 | 5-6 | 10 | 24 | uniform | 4-5 | uniform/llm | 10⁵⁰⁷ | | | 17 | 5-6 | 10 | 26 | uniform | 4-5 | uniform/llm | 10⁵⁴⁹ | | | 18 | 5-6 | 12 | 28 | uniform | 4-5 | uniform/llm | 10⁸⁰⁵ | | | 19 | 5-6 | 12 | 30 | uniform | 5 | uniform/llm | 10⁸⁶¹ | | | 20 | 5-6 | 12 | 32 | uniform | 5 | uniform/llm | 10⁹¹⁹ | *SLR-Bench Curriculum: level-wise configurations, detailing language and task parameters for each difficulty stage. Language complexity is systematically increased by expanding the number of car constants and predicates. Task configuration grows via adapting problem size, background sampling, rule length, and rule sampling strategy. The final column reports the approximate combinatorial size of unique tasks available at each level.* --- ## Licensing Information SLR-Bench is made available under the [CC BY](https://creativecommons.org/licenses/by/4.0/) license. ## Citation If you use this dataset or framework, please cite: ```bibtex @incollection{helff2025slrautomatedsynthesisscalable, title={SLR: Automated Synthesis for Scalable Logical Reasoning}, author={Lukas Helff and Ahmad Omar and Felix Friedrich and Antonia Wüst and Hikaru Shindo and Rupert Mitchell and Tim Woydt and Patrick Schramowski and Wolfgang Stammer and Kristian Kersting}, year={2025}, booktitle ={Working Notes of the NeurIPS Workshop on Foundations of Reasoning in Language Models}, url={https://arxiv.org/abs/2506.15787}, } ``` ---