
{"id":27119,"date":"2024-02-29T04:19:26","date_gmt":"2024-02-29T05:19:26","guid":{"rendered":"https:\/\/express24.ir\/d\/product\/%d9%85%d9%82%d8%a7%d9%84%d9%87-%d9%85%d8%a7%d9%86%d9%86%d8%af-%db%8c%da%a9-%d8%a8%d9%88%d9%85%db%8c-%d8%b5%d8%ad%d8%a8%d8%aa-%da%a9%d9%86%db%8c%d8%af-%d8%a7%d8%b1%d8%a7%d8%a6%d9%87-%d9%85%d8%af%d9%84\/"},"modified":"2024-02-29T04:19:26","modified_gmt":"2024-02-29T05:19:26","slug":"%d9%85%d9%82%d8%a7%d9%84%d9%87-%d9%85%d8%a7%d9%86%d9%86%d8%af-%db%8c%da%a9-%d8%a8%d9%88%d9%85%db%8c-%d8%b5%d8%ad%d8%a8%d8%aa-%da%a9%d9%86%db%8c%d8%af-%d8%a7%d8%b1%d8%a7%d8%a6%d9%87-%d9%85%d8%af%d9%84","status":"publish","type":"product","link":"https:\/\/express24.ir\/d\/product\/%d9%85%d9%82%d8%a7%d9%84%d9%87-%d9%85%d8%a7%d9%86%d9%86%d8%af-%db%8c%da%a9-%d8%a8%d9%88%d9%85%db%8c-%d8%b5%d8%ad%d8%a8%d8%aa-%da%a9%d9%86%db%8c%d8%af-%d8%a7%d8%b1%d8%a7%d8%a6%d9%87-%d9%85%d8%af%d9%84\/","title":{"rendered":"\u0645\u0642\u0627\u0644\u0647 \u0645\u0627\u0646\u0646\u062f \u06cc\u06a9 \u0628\u0648\u0645\u06cc \u0635\u062d\u0628\u062a \u06a9\u0646\u06cc\u062f: \u0627\u0631\u0627\u0626\u0647 \u0645\u062f\u0644 \u0647\u0627\u06cc \u0632\u0628\u0627\u0646\u06cc \u0628\u0632\u0631\u06af \u0628\u0647 \u0633\u0628\u06a9 \u0628\u0648\u0645\u06cc"},"content":{"rendered":"<table class=\"table table-striped table-hover\">\n<tbody>\n<tr>\n<td>\u0639\u0646\u0648\u0627\u0646 \u0645\u0642\u0627\u0644\u0647 \u0628\u0647 \u0627\u0646\u06af\u0644\u06cc\u0633\u06cc <\/td>\n<td>Speak Like a Native: Prompting Large Language Models in a Native Style<\/td>\n<\/tr>\n<tr>\n<td>\u0639\u0646\u0648\u0627\u0646 \u0645\u0642\u0627\u0644\u0647 \u0628\u0647 \u0641\u0627\u0631\u0633\u06cc <\/td>\n<td>\u0645\u0642\u0627\u0644\u0647 \u0645\u0627\u0646\u0646\u062f \u06cc\u06a9 \u0628\u0648\u0645\u06cc \u0635\u062d\u0628\u062a \u06a9\u0646\u06cc\u062f: \u0627\u06cc\u062c\u0627\u062f \u0645\u062f\u0644 \u0647\u0627\u06cc \u0632\u0628\u0627\u0646\u06cc \u0628\u0632\u0631\u06af \u062f\u0631 \u0633\u0628\u06a9 \u0628\u0648\u0645\u06cc<\/td>\n<\/tr>\n<tr>\n<td>\u0646\u0648\u06cc\u0633\u0646\u062f\u06af\u0627\u0646 <\/td>\n<td>Zhicheng Yang, Yiwei Wang, Yinya Huang, Jing Xiong, Xiaodan Liang, Jing Tang<\/td>\n<\/tr>\n<tr>\n<td>\u0632\u0628\u0627\u0646 \u0645\u0642\u0627\u0644\u0647 <\/td>\n<td>\u0627\u0646\u06af\u0644\u06cc\u0633\u06cc<\/td>\n<\/tr>\n<tr>\n<td>\u0641\u0631\u0645\u062a \u0645\u0642\u0627\u0644\u0647: <\/td>\n<td>PDF<\/td>\n<\/tr>\n<tr>\n<td>\u062a\u0639\u062f\u0627\u062f \u0635\u0641\u062d\u0627\u062a<\/td>\n<td>0<\/td>\n<\/tr>\n<tr>\n<td>\u062f\u0633\u062a\u0647 \u0628\u0646\u062f\u06cc \u0645\u0648\u0636\u0648\u0639\u0627\u062a  <\/td>\n<td>Artificial Intelligence,Machine Learning,\u0647\u0648\u0634 \u0645\u0635\u0646\u0648\u0639\u06cc , \u06cc\u0627\u062f\u06af\u06cc\u0631\u06cc \u0645\u0627\u0634\u06cc\u0646 ,<\/td>\n<\/tr>\n<tr>\n<td>\u062a\u0648\u0636\u06cc\u062d\u0627\u062a    <\/td>\n<td>Submitted 22 November, 2023; originally announced November 2023. , Comments: 8 pages, 3 figures<\/td>\n<\/tr>\n<tr>\n<td>\u062a\u0648\u0636\u06cc\u062d\u0627\u062a \u0628\u0647 \u0641\u0627\u0631\u0633\u06cc    <\/td>\n<td>\u0627\u0631\u0633\u0627\u0644 \u0634\u062f\u0647 22 \u0646\u0648\u0627\u0645\u0628\u0631 2023 \u061b\u062f\u0631 \u0627\u0628\u062a\u062f\u0627 \u0646\u0648\u0627\u0645\u0628\u0631 2023 \u0627\u0639\u0644\u0627\u0645 \u0634\u062f \u060c \u0646\u0638\u0631\u0627\u062a: 8 \u0635\u0641\u062d\u0647 \u060c 3 \u0634\u06a9\u0644<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<h2>\u0686\u06a9\u06cc\u062f\u0647<\/h2>\n<p style=\"direction:ltr;\">Existing work has found that the prompt engineering heavily influences the performance of large language models (LLMs). Chain-of-thought (CoT), as a popular prompt engineering technique, prompted LLMs using in-context examples with reasoning steps. In current studies, the few-shot examples of CoT are generally handcrafted by humans. However, how the text style of in-context examples influence the outputs of LLMs still remains under-explored. This paper presents a novel and effective approach, named \\textbf{AlignCoT}, to improve the reasoning capability of LLMs by aligning the in-context examples with the native style of LLMs. &#8220;Native&#8221; refers to the inherent characteristic style of LLMs which can be probed by original zero-shot scenarios. AlignCoT is orthogonal to other prompt engineering methods, making it easy to combine with state-of-the-art techniques to further improve the LLMs&#8217; performance. We conduct extensive and comprehensive experiments on several benchmarks. The empirical results demonstrate that our AlignCoTsignificantly improves performance over the carefully handcrafted in-context examples. For instance, with GPT-3.5-turbo, we observed a +2.5\\% improvement on GSM8K. Furthermore, our AlignCoT consistently improve the performance when combined with other state-of-the-art prompt engineering methods. The source code and dataset will be available at \\href{https:\/\/github.com\/yangzhch6\/AlignCoT}{https:\/\/github.com\/yangzhch6\/AlignCoT}.<\/p>\n<h2>\u0686\u06a9\u06cc\u062f\u0647 \u0628\u0647 \u0641\u0627\u0631\u0633\u06cc (\u062a\u0631\u062c\u0645\u0647 \u0645\u0627\u0634\u06cc\u0646\u06cc)<\/h2>\n<p>\u06a9\u0627\u0631 \u0645\u0648\u062c\u0648\u062f \u0646\u0634\u0627\u0646 \u062f\u0627\u062f\u0647 \u0627\u0633\u062a \u06a9\u0647 \u0645\u0647\u0646\u062f\u0633\u06cc \u0633\u0631\u06cc\u0639 \u0628\u0647 \u0634\u062f\u062a \u0628\u0631 \u0639\u0645\u0644\u06a9\u0631\u062f \u0645\u062f\u0644\u0647\u0627\u06cc \u0628\u0632\u0631\u06af \u0632\u0628\u0627\u0646 (LLMS) \u062a\u0623\u062b\u06cc\u0631 \u0645\u06cc \u06af\u0630\u0627\u0631\u062f.\u0632\u0646\u062c\u06cc\u0631\u0647 \u0627\u06cc \u0627\u0632 \u0641\u06a9\u0631 (COT) \u060c \u0628\u0647 \u0639\u0646\u0648\u0627\u0646 \u06cc\u06a9 \u062a\u06a9\u0646\u06cc\u06a9 \u0645\u062d\u0628\u0648\u0628 \u0645\u0647\u0646\u062f\u0633\u06cc \u0633\u0631\u06cc\u0639 \u060c LLM \u0647\u0627 \u0631\u0627 \u0628\u0627 \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u0627\u0632 \u0646\u0645\u0648\u0646\u0647 \u0647\u0627\u06cc \u062f\u0631\u0648\u0646 \u0645\u062a\u0646 \u0628\u0627 \u0645\u0631\u0627\u062d\u0644 \u0627\u0633\u062a\u062f\u0644\u0627\u0644 \u0628\u0631\u0627\u0646\u06af\u06cc\u062e\u062a.\u062f\u0631 \u0645\u0637\u0627\u0644\u0639\u0627\u062a \u0641\u0639\u0644\u06cc \u060c \u0646\u0645\u0648\u0646\u0647 \u0647\u0627\u06cc \u0686\u0646\u062f \u0639\u06a9\u0633 \u0627\u0632 COT \u0628\u0647 \u0637\u0648\u0631 \u06a9\u0644\u06cc \u062a\u0648\u0633\u0637 \u0627\u0646\u0633\u0627\u0646 \u0633\u0627\u062e\u062a\u0647 \u0645\u06cc \u0634\u0648\u0646\u062f.\u0628\u0627 \u0627\u06cc\u0646 \u062d\u0627\u0644 \u060c \u0686\u06af\u0648\u0646\u0647 \u0633\u0628\u06a9 \u0645\u062a\u0646 \u0646\u0645\u0648\u0646\u0647 \u0647\u0627\u06cc \u062f\u0631\u0648\u0646 \u0645\u062a\u0646 \u0628\u0631 \u062e\u0631\u0648\u062c\u06cc \u0647\u0627\u06cc LLM \u0647\u0627 \u062a\u0623\u062b\u06cc\u0631 \u0645\u06cc \u06af\u0630\u0627\u0631\u062f \u060c \u0647\u0646\u0648\u0632 \u062a\u062d\u062a \u062a\u0623\u062b\u06cc\u0631 \u0642\u0631\u0627\u0631 \u0646\u0645\u06cc \u06af\u06cc\u0631\u062f.\u062f\u0631 \u0627\u06cc\u0646 \u0645\u0642\u0627\u0644\u0647 \u06cc\u06a9 \u0631\u0648\u06cc\u06a9\u0631\u062f \u062c\u062f\u06cc\u062f \u0648 \u0645\u0624\u062b\u0631 \u0628\u0627 \u0646\u0627\u0645 \\ textbf {aligncot} \u060c \u0628\u0631\u0627\u06cc \u0628\u0647\u0628\u0648\u062f \u062a\u0648\u0627\u0646\u0627\u06cc\u06cc \u0627\u0633\u062a\u062f\u0644\u0627\u0644 LLM \u0647\u0627 \u0628\u0627 \u062a\u0631\u0627\u0632 \u06a9\u0631\u062f\u0646 \u0646\u0645\u0648\u0646\u0647 \u0647\u0627\u06cc \u062f\u0631\u0648\u0646 \u0645\u062a\u0646 \u0628\u0627 \u0633\u0628\u06a9 \u0628\u0648\u0645\u06cc LLMS \u0627\u0631\u0627\u0626\u0647 \u0634\u062f\u0647 \u0627\u0633\u062a.&#8221;\u0628\u0648\u0645\u06cc&#8221; \u0628\u0647 \u0633\u0628\u06a9 \u0645\u0634\u062e\u0635\u0647 \u0630\u0627\u062a\u06cc LLM \u0647\u0627 \u0627\u0634\u0627\u0631\u0647 \u062f\u0627\u0631\u062f \u06a9\u0647 \u0645\u06cc \u062a\u0648\u0627\u0646\u062f \u062a\u0648\u0633\u0637 \u0633\u0646\u0627\u0631\u06cc\u0648\u0647\u0627\u06cc \u0627\u0635\u0644\u06cc \u0635\u0641\u0631 \u0645\u0648\u0631\u062f \u0628\u0631\u0631\u0633\u06cc \u0642\u0631\u0627\u0631 \u06af\u06cc\u0631\u062f.AlignCot \u0628\u0627 \u0633\u0627\u06cc\u0631 \u0631\u0648\u0634\u0647\u0627\u06cc \u0645\u0647\u0646\u062f\u0633\u06cc \u0633\u0631\u06cc\u0639 \u0645\u062a\u0639\u0627\u0645\u062f \u0627\u0633\u062a \u060c \u0648 \u062a\u0631\u06a9\u06cc\u0628 \u0622\u0646 \u0628\u0627 \u062a\u06a9\u0646\u06cc\u06a9 \u0647\u0627\u06cc \u067e\u06cc\u0634\u0631\u0641\u062a\u0647 \u0628\u0631\u0627\u06cc \u0628\u0647\u0628\u0648\u062f \u0628\u06cc\u0634\u062a\u0631 \u0639\u0645\u0644\u06a9\u0631\u062f LLMS \u0631\u0627 \u0622\u0633\u0627\u0646 \u0645\u06cc \u06a9\u0646\u062f.\u0645\u0627 \u0622\u0632\u0645\u0627\u06cc\u0634 \u0647\u0627\u06cc \u06af\u0633\u062a\u0631\u062f\u0647 \u0648 \u062c\u0627\u0645\u0639 \u0631\u0627 \u062f\u0631 \u0645\u0648\u0631\u062f \u0686\u0646\u062f\u06cc\u0646 \u0645\u0639\u06cc\u0627\u0631 \u0627\u0646\u062c\u0627\u0645 \u0645\u06cc \u062f\u0647\u06cc\u0645.\u0646\u062a\u0627\u06cc\u062c \u062a\u062c\u0631\u0628\u06cc \u0646\u0634\u0627\u0646 \u0645\u06cc \u062f\u0647\u062f \u06a9\u0647 \u062a\u0631\u0627\u0632 \u0645\u0627 \u0628\u0647 \u0637\u0648\u0631 \u0642\u0627\u0628\u0644 \u062a\u0648\u062c\u0647\u06cc \u0639\u0645\u0644\u06a9\u0631\u062f \u0631\u0627 \u062f\u0631 \u0646\u0645\u0648\u0646\u0647 \u0647\u0627\u06cc \u0628\u0627 \u062f\u0642\u062a \u062f\u0633\u062a \u0633\u0627\u0632 \u062f\u0631 \u0645\u062a\u0646 \u0628\u0647\u0628\u0648\u062f \u0645\u06cc \u0628\u062e\u0634\u062f.\u0628\u0647 \u0639\u0646\u0648\u0627\u0646 \u0645\u062b\u0627\u0644 \u060c \u0628\u0627 GPT-5.5 \u062a\u0648\u0631\u0628\u0648 \u060c \u0645\u0627 \u06cc\u06a9 \u067e\u06cc\u0634\u0631\u0641\u062a +2.5 \\ \u066a \u062f\u0631 GSM8K \u0631\u0627 \u0645\u0634\u0627\u0647\u062f\u0647 \u06a9\u0631\u062f\u06cc\u0645.\u0639\u0644\u0627\u0648\u0647 \u0628\u0631 \u0627\u06cc\u0646 \u060c AlignCot \u0645\u0627 \u0628\u0647 \u0637\u0648\u0631 \u0645\u062f\u0627\u0648\u0645 \u0639\u0645\u0644\u06a9\u0631\u062f \u0631\u0627 \u062f\u0631 \u0647\u0646\u06af\u0627\u0645 \u062a\u0631\u06a9\u06cc\u0628 \u0628\u0627 \u0633\u0627\u06cc\u0631 \u0631\u0648\u0634\u0647\u0627\u06cc \u067e\u06cc\u0634\u0631\u0641\u062a\u0647 \u0645\u0647\u0646\u062f\u0633\u06cc \u0633\u0631\u06cc\u0639 \u0628\u0647\u0628\u0648\u062f \u0645\u06cc \u0628\u062e\u0634\u062f.\u06a9\u062f \u0645\u0646\u0628\u0639 \u0648 \u0645\u062c\u0645\u0648\u0639\u0647 \u062f\u0627\u062f\u0647 \u0647\u0627 \u062f\u0631 \\ href {https:\/\/github.com\/yangzhch6\/aligncot} {https:\/\/github.com\/yangzhch6\/aligncot} \u062f\u0631 \u062f\u0633\u062a\u0631\u0633 \u062e\u0648\u0627\u0647\u062f \u0628\u0648\u062f.<\/p>\n<table class=\"table table-bordered table-primary\">\r\n<tr>\r\n\t<td>\r\n\t\u062a\u0648\u062c\u0647 \u06a9\u0646\u06cc\u062f \u0627\u06cc\u0646 \u0645\u0642\u0627\u0644\u0647 \u0628\u0647 \u0632\u0628\u0627\u0646 \u0627\u0646\u06af\u0644\u06cc\u0633\u06cc \u0627\u0633\u062a.\r\n\t<\/td>\r\n<\/tr>\r\n\t\r\n<tr>\r\n\t<td>\r\n     \u0628\u0631\u0627\u06cc \u0633\u0641\u0627\u0631\u0634 \u062a\u0631\u062c\u0645\u0647 \u0627\u06cc\u0646 \u0645\u0642\u0627\u0644\u0647 \u0645\u06cc \u062a\u0648\u0627\u0646\u06cc\u062f \u0628\u0647 \u06cc\u06a9\u06cc \u0627\u0632 \u0631\u0648\u0634 \u0647\u0627\u06cc \u062a\u0645\u0627\u0633\u060c \u067e\u06cc\u0627\u0645\u06a9\u060c \u062a\u0644\u06af\u0631\u0627\u0645 \u0648 \u06cc\u0627 \u0648\u0627\u062a\u0633 \u0627\u067e \u0628\u0627 \u0634\u0645\u0627\u0631\u0647 \u0632\u06cc\u0631 \u062a\u0645\u0627\u0633 \u0628\u06af\u06cc\u0631\u06cc\u062f:\r\n\t<br \/><br \/>\r\n\t09395106248\r\n\t\t\r\n\t\t\r\n\t<br \/><br \/>\r\n\t\t\u062a\u0648\u062c\u0647 \u06a9\u0646\u06cc\u062f \u06a9\u0647 \u0634\u0631\u0627\u06cc\u0637 \u062a\u0631\u062c\u0645\u0647 \u0628\u0647 \u0635\u0648\u0631\u062a \u0632\u06cc\u0631 \u0627\u0633\u062a:\r\n\t\t<br \/>\r\n\t\t<ul>\r\n\t\t<li>\r\n\t\t\u0642\u06cc\u0645\u062a \u0647\u0631 \u0635\u0641\u062d\u0647 \u062a\u0631\u062c\u0645\u0647 \u062f\u0631 \u062d\u0627\u0644 \u062d\u0627\u0636\u0631 40 \u0647\u0632\u0627\u0631 \u062a\u0648\u0645\u0627\u0646 \u0645\u06cc \u0628\u0627\u0634\u062f. \r\n\t\t<\/li>\r\n\t\t<li>\r\n\t\t\u062a\u062d\u0648\u06cc\u0644 \u0645\u0642\u0627\u0644\u0647 \u062a\u0631\u062c\u0645\u0647 \u0634\u062f\u0647 \u0628\u0647 \u0635\u0648\u0631\u062a \u0641\u0627\u06cc\u0644 \u0648\u0631\u062f \u0645\u06cc \u0628\u0627\u0634\u062f.\r\n\t\t<\/li>\r\n\t\t<li>\r\n\t\t\u0632\u0645\u0627\u0646 \u062a\u062d\u0648\u06cc\u0644 \u062a\u0631\u062c\u0645\u0647 \u0645\u0642\u0627\u0644\u0647 \u062f\u0631 \u0635\u0648\u0631\u062a \u062f\u0627\u0634\u062a\u0646 \u062a\u0639\u062f\u0627\u062f \u0635\u0641\u062d\u0627\u062a \u0639\u0627\u062f\u06cc \u0628\u06cc\u0646 3 \u062a\u0627 5 \u0631\u0648\u0632 \u062e\u0648\u0627\u0647\u062f \u0628\u0648\u062f.\r\n\t\t<\/li>\r\n\t\t\r\n\t\t<li>\r\n\t\t\u06a9\u06cc\u0641\u06cc\u062a \u062a\u0631\u062c\u0645\u0647 \u0628\u0633\u06cc\u0627\u0631 \u0628\u0627\u0644\u0627 \u0645\u06cc \u0628\u0627\u0634\u062f. \u0645\u0642\u0627\u0644\u0647 \u0641\u0642\u0637 \u062a\u0648\u0633\u0637 \u0645\u062a\u0631\u062c\u0645\u06cc\u0646 \u0628\u0627 \u0645\u062f\u0631\u06a9 \u062f\u0627\u0646\u0634\u06af\u0627\u0647\u06cc \u0645\u062a\u0631\u062c\u0645\u06cc \u062a\u0631\u062c\u0645\u0647 \u0645\u06cc\u200c\u0634\u0648\u062f.\r\n\t\t<\/li>\r\n\t\t<li>\r\n\t\t\u06a9\u0644\u06cc\u0647 \u062c\u062f\u0627\u0648\u0644 \u0648 \u0641\u0631\u0645\u0648\u0644 \u0647\u0627 \u0646\u06cc\u0632 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