| --- |
| license: mit |
| task_categories: |
| - text-classification |
| language: |
| - he |
| pretty_name: LCHAIM |
| size_categories: |
| - 1K<n<10K |
| --- |
| ## LCHAIM: Investigating Long Context Reasoning in Hebrew |
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| ### Overview |
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| LCHAIM is a dataset designed to evaluate Natural Language Inference (NLI) models in Hebrew. Unlike English, Hebrew is a Morphologically Rich Language (MRL), requiring more research to develop robust NLI models. LCHAIM provides a benchmark for models that need to handle long premises and complex reasoning in Hebrew. |
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| ### Dataset Description |
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| LCHAIM was created by translating and validating the English ConTRoL dataset into Hebrew. It consists of 8,325 context-hypothesis pairs that require various types of reasoning, including: |
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| * Coreferential reasoning |
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| * Temporal reasoning |
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| * Logical reasoning |
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| * Analytical reasoning |
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| ### Performance Benchmarks |
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| Experiments with LCHAIM highlight the challenges of contextual reasoning in Hebrew. Key results include: |
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| Fine-tuning the LongHero model on both Hebrew NLI datasets and LCHAIM yielded a mean accuracy of 52%, which is 35% (absolute) lower than human performance. |
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| Large Language Models (LLMs) in a few-shot setting achieved the following top mean accuracies: |
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| * Gemma-9B |
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| * Dicta-LM-2.0-7B |
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| * GPT-4o |
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| Top performance: 60.12% mean accuracy |
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| ### Citation |
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| If you use LCHAIM in your research, please cite our work: |
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| ``` |
| @inproceedings{malul2025lchaim, |
| title={Lchaim-investigating long context reasoning in hebrew}, |
| author={Malul, Ehud and Perets, Oriel and Mor, Ziv and Kassel, Yigal and Sulem, Elior}, |
| booktitle={Findings of the Association for Computational Linguistics: ACL 2025}, |
| pages={7928--7939}, |
| year={2025} |
| } |
| ``` |
| ### License |
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| LCHAIM is released under the mit license. |
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| ### Contact |
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| For questions or feedback, please contact orielpe@post.bgu.ac.il |
