A Perspective on Large Language Models, Intelligent Machines, and Knowledge Acquisition
<|reference_start|>A Perspective on Large Language Models, Intelligent Machines, and Knowledge Acquisition: Large Language Models (LLMs) are known for their remarkable ability to generate synthesized 'knowledge', such as text documents, music, images, etc. However, there is a huge gap between LLM's and human capabilities for understanding abstract concepts and reasoning. We discuss these issues in a larger philosophical context of human knowledge acquisition and the Turing test. In addition, we illustrate the limitations of LLMs by analyzing GPT-4 responses to questions ranging from science and math to common sense reasoning. These examples show that GPT-4 can often imitate human reasoning, even though it lacks understanding. However, LLM responses are synthesized from a large LLM model trained on all available data. In contrast, human understanding is based on a small number of abstract concepts. Based on this distinction, we discuss the impact of LLMs on acquisition of human knowledge and education.<|reference_end|>
@article{cherkassky2024a,
    title={A Perspective on Large Language Models, Intelligent Machines, and
  Knowledge Acquisition},
    author={Vladimir Cherkassky and Eng Hock Lee},
    journal={arXiv preprint arXiv:2408.06598},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.06598},
    primaryClass={cs.CL cs.AI}
}