Create README.md

README.md

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+---
+language:
+- fa
+license: cc-by-sa-4.0
+tags:
+- evaluation
+- multilingual
+pretty_name: Multi-LMentry
+task_categories:
+- question-answering
+configs:
+  - config_name: fa
+    data_files: fa/*.jsonl
+dataset_info:
+  features:
+    - name: id
+      dtype: string
+    - name: input
+      dtype: string
+    - name: metadata
+      dtype: string
+    - name: canary
+      dtype: string
+  splits:
+    - name: test
+---
+
+
+# Multi-LMentry
+
+This dataset card provides documentation for **Multi-LMentry**, a multilingual benchmark designed for evaluating large language models (LLMs) on fundamental, elementary-level tasks across nine languages. It is the official dataset release accompanying the EMNLP 2025 paper "Multi-LMentry: Can Multilingual LLMs Solve Elementary Tasks Across Languages?".
+
+## Dataset Details
+
+### Dataset Description
+
+Multi-LMentry is a multilingual extension of [LMentry (Efrat et al., 2023)](https://aclanthology.org/2023.findings-acl.666/), which evaluates LLMs on tasks that are trivial for humans but often challenging for models. It covers **nine languages**:  
+- Farsi
+
+The dataset enables systematic evaluation of core model abilities across low-, mid-, and high-resource languages. Tasks were recreated manually with the help of native speakers, ensuring linguistic and cultural appropriateness rather than relying on direct translation.
+
+### Dataset Sources
+
+- **Paper:** Accepted at EMNLP 2025 main conference (link pending)
+- [**GitHub Repository:**](https://github.com/langtech-bsc/multi_lmentry) Code to perform the evaluation on Multi-LMentry
+
+## Uses
+
+The dataset is intended for:  
+- **Evaluation of LLMs** on elementary reasoning and understanding tasks.  
+- **Cross-lingual comparisons**, especially between high-resource and low-resource languages.  
+- **Diagnostics / unit tests** of fundamental model abilities.  
+
+It is **not intended** for training language models directly.  
+
+## Dataset Structure
+
+- The dataset is organized by **language folders**.  
+- Inside each folder, there is **one JSON file per task**.  
+- Each JSON contains input prompts and expected outputs for that task.  
+- Tasks include simple sentence construction, contextual word choice, alphabetic reasoning, etc.  
+- Some tasks are language-specific (e.g., rhyming words are excluded where not applicable).  
+
+## How to Use
+
+```
+from datasets import load_dataset
+import json
+
+# Load the Spanish "bigger_number" task
+ds = load_dataset(
+    "BSC-LT/multi_lmentry",
+    "fa",
+    data_files="fa/bigger_number.jsonl"
+)["train"]
+
+# Access first example
+example = ds[0]
+print("Input:", example["input"])
+
+# Convert metadata from string to dictionary
+metadata = json.loads(example["metadata"])
+print("Metadata:", metadata)
+
+# Access the answer from metadata
+answer = metadata.get("answer")
+print("Answer:", answer)
+``` 
+
+**Notes**:
+
+- The metadata field contains task-specific information, including the answer. Its structure varies depending on the task, for example:
+  - Multiple-choice tasks may include a list of distractors and the correct answer index.
+  - Open-ended tasks, like "ends_with_letter", may only include task-specific metadata such as the target letter, without a predefined answer.
+  - Other fields (e.g., num_digits, n1, n2, template_id) may differ depending on the task type.
+- Each JSONL file corresponds to a specific task; you can load multiple tasks by specifying multiple data_files.
+- Evaluation: Multi-LMentry includes manually crafted regexes for each task to automatically check answers. These evaluation scripts are available in the (GitHub repository)[https://github.com/langtech-bsc/multi_lmentry] and ready to use for running systematic assessments of model outputs.
+
+## Dataset Creation
+
+### Curation Rationale
+
+The motivation is to provide a **systematic, multilingual benchmark** for assessing whether LLMs can perform **basic reasoning tasks** that humans—even with only elementary proficiency—find trivial. This is crucial since many evaluations today focus on high-level reasoning while overlooking core capabilities.
+
+### Source Data
+
+#### Data Collection and Processing
+
+- Data was **manually created** in each language, rather than translated from English.  
+- Native speakers were involved to ensure correctness, cultural relevance, and avoidance of ambiguity or bias.  
+- Tasks were adapted to respect **linguistic characteristics**, such as orthography, morphology, or alphabet differences.
+
+#### Who are the source data producers?
+
+- **Native speakers** of the target languages, who carefully designed and validated the tasks.  
+- Task designs follow the original LMentry methodology but were recreated independently per language by native speakers of the target languages, who carefully designed and validated the tasks. 
+
+## Acknowledgements
+
+We gratefully acknowledge the support of Future AI Research ([PNRR MUR project PE0000013-FAIR](https://fondazione-fair.it/en/)).
+ 
+The authors gratefully acknowledge the support of the AI Factory IT4LIA project and the CINECA award FAIR_NLP under the ISCRA initiative for granting access high-performance computing resources.
+
+This work is funded by the Ministerio para la Transformación Digital y de la Función Pública and Plan de Recuperación, Transformación y Resiliencia - Funded by EU – NextGenerationEU within the framework of the project ILENIA with references 2022/TL22/00215337, 2022/TL22/00215336 and 2022/TL22/00215335, and within the framework of the project Desarrollo Modelos ALIA.
+
+This work has been promoted and financed by the Generalitat de Catalunya through the Aina project. 
+
+## License Information
+
+[CC BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0/deed.ca)
+
+## Citation
+
+### Bibtex
+
+```bibtex
+@inproceedings{moroni-etal-2025-multi,
+    title = "Multi-{LM}entry: Can Multilingual {LLM}s Solve Elementary Tasks Across Languages?",
+    author = "Moroni, Luca  and
+      Aula-Blasco, Javier  and
+      Conia, Simone  and
+      Baucells, Irene  and
+      Perez, Naiara  and
+      Su{\'a}rez, Silvia Paniagua  and
+      Sall{\'e}s, Anna  and
+      Ostendorff, Malte  and
+      Falc{\~a}o, J{\'u}lia  and
+      Son, Guijin  and
+      Gonzalez-Agirre, Aitor  and
+      Navigli, Roberto  and
+      Villegas, Marta",
+    editor = "Christodoulopoulos, Christos  and
+      Chakraborty, Tanmoy  and
+      Rose, Carolyn  and
+      Peng, Violet",
+    booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing",
+    month = nov,
+    year = "2025",
+    address = "Suzhou, China",
+    publisher = "Association for Computational Linguistics",
+    url = "https://aclanthology.org/2025.emnlp-main.1731/",
+    doi = "10.18653/v1/2025.emnlp-main.1731",
+    pages = "34114--34145",
+    ISBN = "979-8-89176-332-6"
+}
+```