## LCHAIM: A Hebrew Natural Language Inference Dataset

### 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, which should be published in the next month

### License


LCHAIM is released under the mit license.

### Contact

For questions or feedback, please contact orielpe@post.bgu.ac.il