Case Study: Testing Model Capabilities in Some Reasoning Tasks

Min Zhang, Sato Takumi, Jack Zhang, Jun Wang
2/15/2024
cs.CL

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Abstract

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.

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Cite this paper

@article{zhang2024case,
  title  = {Case Study: Testing Model Capabilities in Some Reasoning Tasks},
  author = {Min Zhang and Sato Takumi and Jack Zhang and Jun Wang},
  year   = {2024},
  eprint = {2402.09967},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2402.09967v1}
}

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