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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2402.09967 |
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| _version_ | 1866910332530720768 |
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| author | Zhang, Min Takumi, Sato Zhang, Jack Wang, Jun |
| author_facet | Zhang, Min Takumi, Sato Zhang, Jack Wang, Jun |
| contents | Large Language Models (LLMs) excel in generating personalized content and facilitating interactive dialogues, showcasing their remarkable aptitude for a myriad of applications. However, their capabilities in reasoning and providing explainable outputs, especially within the context of reasoning abilities, remain areas for improvement. In this study, we delve into the reasoning abilities of LLMs, highlighting the current challenges and limitations that hinder their effectiveness in complex reasoning scenarios. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_09967 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Case Study: Testing Model Capabilities in Some Reasoning Tasks Zhang, Min Takumi, Sato Zhang, Jack Wang, Jun Computation and Language Large Language Models (LLMs) excel in generating personalized content and facilitating interactive dialogues, showcasing their remarkable aptitude for a myriad of applications. However, their capabilities in reasoning and providing explainable outputs, especially within the context of reasoning abilities, remain areas for improvement. In this study, we delve into the reasoning abilities of LLMs, highlighting the current challenges and limitations that hinder their effectiveness in complex reasoning scenarios. |
| title | Case Study: Testing Model Capabilities in Some Reasoning Tasks |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2402.09967 |