This article delves into the top ten challenges in the research of large language models (LLMs), including reducing and measuring hallucinations, optimizing context length and construction, integrating other data modalities, enhancing the speed and lowering the cost of LLMs, designing new model architectures, developing alternatives to GPUs, improving the usability of agents, enhancing the ability to learn from human preferences, increasing the efficiency of chat interfaces, and constructing LLMs for non-English languages. Among these, reducing hallucinations and context learning are currently the most popular directions. Multimodality, new architectures, and GPU alternatives also hold significant potential. Overall, LLM research is in a phase of rapid development, with exploration flourishing in various directions.