Large language models have become an important research direction in artificial intelligence in recent years. These models are usually based on the Transformer architecture and are trained on massive text datasets. By learning statistical patterns in language, large language models can perform various tasks such as text generation, question answering, and machine translation. As model sizes continue to increase, large language models have achieved performance close to or even surpassing human levels in many natural language processing tasks. However, training and deploying such large models require enormous computational resources, which leads to concerns about energy consumption and cost. Therefore, improving model efficiency has become an important research focus.
- 近年来大语言模型已经在人工智能领域里成为一个重要的研究方向。这些模型通常基于Transformer架构并在大规模文本数据集上训练。通过学习语言中的统计学模式，大语言模型能执行多种任务，例如文本生成，问答和机器翻译。随着模型规模的持续增长，大语言模型已经在许多自然语言处理任务中取得了和人类相近甚至超过的水平。然而，训练和部署这样的大模型需要庞大的计算资源，这引发了对能源消耗和金钱开销的担忧。因此，提高模型效率已经成为一个重要研究点