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| Auteurs principaux: | , |
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| Format: | Preprint |
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2024
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| Accès en ligne: | https://arxiv.org/abs/2407.18968 |
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| _version_ | 1866909270180626432 |
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| author | Galanti, Liane Baron, Ethan |
| author_facet | Galanti, Liane Baron, Ethan |
| contents | In this project, we test the effectiveness of Large Language Models (LLMs) on the Abstraction and Reasoning Corpus (ARC) dataset. This dataset serves as a representative benchmark for testing abstract reasoning abilities, requiring a fundamental understanding of key concepts such as object identification, basic counting, and elementary geometric principles. Tasks from this dataset are converted into a prompt-based format for evaluation. Initially, we assess the models' potential through a Zero-shot approach. Subsequently, we investigate the application of the Chain-of-Thought (CoT) technique, aiming to determine its role in improving model performance. Our results suggest that, despite the high expectations placed on contemporary LLMs, these models still struggle in non-linguistic domains, even when dealing with simpler subsets of the ARC dataset. Our study is the first to concentrate on the capabilities of open-source models in this context. The code, dataset, and prompts supporting this project's findings can be found in our GitHub repository, accessible at: https://github.com/Lianga2000/LLMsOnARC. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_18968 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Intelligence Analysis of Language Models Galanti, Liane Baron, Ethan Artificial Intelligence In this project, we test the effectiveness of Large Language Models (LLMs) on the Abstraction and Reasoning Corpus (ARC) dataset. This dataset serves as a representative benchmark for testing abstract reasoning abilities, requiring a fundamental understanding of key concepts such as object identification, basic counting, and elementary geometric principles. Tasks from this dataset are converted into a prompt-based format for evaluation. Initially, we assess the models' potential through a Zero-shot approach. Subsequently, we investigate the application of the Chain-of-Thought (CoT) technique, aiming to determine its role in improving model performance. Our results suggest that, despite the high expectations placed on contemporary LLMs, these models still struggle in non-linguistic domains, even when dealing with simpler subsets of the ARC dataset. Our study is the first to concentrate on the capabilities of open-source models in this context. The code, dataset, and prompts supporting this project's findings can be found in our GitHub repository, accessible at: https://github.com/Lianga2000/LLMsOnARC. |
| title | Intelligence Analysis of Language Models |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2407.18968 |