With the development of large language models (LLMs) like the GPT series,
their widespread use across various application scenarios presents a myriad of
challenges. This review initially explores the issue of domain specificity,
where LLMs may struggle to provide precise answers to specialized questions
within niche fields. The problem of knowledge forgetting arises as these LLMs
might find it hard to balance old and new information. The knowledge repetition
phenomenon reveals that sometimes LLMs might deliver overly mechanized
responses, lacking depth and originality. Furthermore, knowledge illusion
describes situations where LLMs might provide answers that seem insightful but
are actually superficial, while knowledge toxicity focuses on harmful or biased
information outputs. These challenges underscore problems in the training data
and algorithmic design of LLMs. To address these issues, it's suggested to
diversify training data, fine-tune models, enhance transparency and
interpretability, and incorporate ethics and fairness training. Future
technological trends might lean towards iterative methodologies, multimodal
learning, model personalization and customization, and real-time learning and
feedback mechanisms. In conclusion, future LLMs should prioritize fairness,
transparency, and ethics, ensuring they uphold high moral and ethical standards
when serving humanity.

当面临领域特定问题时，大语言模型（LLMs）可能会遇到问题，如知识遗忘、知识重复、知识幻觉以及知识毒性，这些问题突显了 LLMs 的训练数据和算法设计的困扰。为了解决这些问题，建议对训练数据进行多样化，微调模型，提高透明度和可解释性，并引入伦理和公平性培训。未来的技术趋势可能会倾向于迭代方法学、多模态学习、模型个性化定制以及实时学习和反馈机制。最重要的是，未来的 LLMs 应确保在为人类服务时优先考虑公平、透明和伦理，确保它们持有高的道义和道德标准。