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---
license: mit
task_categories:
- text-classification
language:
- he
pretty_name: LCHAIM
size_categories:
- 1K<n<10K
---
## LCHAIM: Investigating Long Context Reasoning in Hebrew
### Overview
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.
### Dataset Description
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:
* Coreferential reasoning
* Temporal reasoning
* Logical reasoning
* Analytical reasoning
### Performance Benchmarks
Experiments with LCHAIM highlight the challenges of contextual reasoning in Hebrew. Key results include:
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.
Large Language Models (LLMs) in a few-shot setting achieved the following top mean accuracies:
* Gemma-9B
* Dicta-LM-2.0-7B
* GPT-4o
Top performance: 60.12% mean accuracy
### Citation
If you use LCHAIM in your research, please cite our work:
```
@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
LCHAIM is released under the mit license.
### Contact
For questions or feedback, please contact orielpe@post.bgu.ac.il |