判断素数改错作者: Turbo时间限制: 1s章节: 循环问题描述给定程序的功能是判断一个整数是否是素数若是输出YES否则输出NO!。请改正程序中的错误使它能得出正确的结果。注意不得增行或删行也不得更改程序的结构#include stdio.hint main() {int m;scanf(%d, m);int k 2;while (k m (m % k))/************found************/k/************found************/if (m k )printf(YES\n);elseprintf(NO\n);return 0;}#include stdio.h int main() { int m; scanf(%d, m); int k 2; while (k m (m % k)) /************found************/ k; /************found************/ if (m k ) printf(YES\n); else printf(NO\n); return 0; }求π的值改错作者: Turbo时间限制: 1s章节: 循环问题描述用下面的公式求π的近似值直到最后一项的绝对值小于指定的精度为止例如程序运行后输入精度0.0001则程序输出3.1414。请改正程序中的错误使它能输出正确的结果。注意不得增行或删行也不得更改程序的结构#include math.h#include stdio.hint main() {float jingdu;int s ;float n, t, pi ;scanf(%f, jingdu);t 1 ;pi 0 ;n 1 ;s 1 ;/**************found**************/while (t jingdu) {pi pi t ;n n 2 ;s -s ;/**************found**************/t s % n;}pi pi * 4 ;printf(%6.4f\n, pi);return 0;}输入说明输入一个实数n输出说明根据源程序所给格式进行输出#include math.h #include stdio.h int main() { float jingdu; int s ; float n, t, pi ; scanf(%f, jingdu); t 1 ; pi 0 ; n 1 ; s 1 ; /**************found**************/ while (fabs(t) jingdu) { pi pi t ; n n 2 ; s -s ; /**************found**************/ t s / n; } pi pi * 4 ; printf(%6.4f\n, pi); return 0; }逆序输出(改错)作者: Turbo时间限制: 1s章节: 指针问题描述给定程序中函数fun的功能是将字符串中的字符按逆序输出但不改变字符串中的内容。例如若字符串为abcd则应输出dcba。请改正程序中的错误使它能计算出正确的结果。注意不要改动main函数不得增行或删行也不得更改程序的结构#include stdio.h/************found************/fun (char a) {if (*a) {fun(a 1);/************found************/printf(%c *a);}}int main() {char s[100] {0};gets(s);fun(s);return 0;}输入说明输入一个字符串输出说明逆序输出字符串#include stdio.h /************found************/ void fun (char *a) { if (*a) { fun(a 1); /************found************/ printf(%c,*a); } } int main() { char s[100] {0}; gets(s); fun(s); return 0; }英语翻译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.大型语言模型是近年来人工智能的一个重要研究方向。这些模型通常基于Transformer体系结构并在大量的文本数据集上进行训练通过学习语言中的统计参数大型语言模型可以执行各种任务如文本生成、问题回答和机器翻译。随着模型大小的不断增加大型语言模型在许多自然语言处理任务中已经取得了接近甚至超过人类水平的性能。然而训练和部署如此庞大的模型需要巨大的计算资源这引发了关于能源消耗和成本的担忧。因此提高模型效率已成为一项重要的研究重点。英语单词