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\u9759\u7684\u306a\u30c6\u30b9\u30c8\u30bb\u30c3\u30c8\u3084\u3001\u30c8\u30ec\u30fc\u30cb\u30f3\u30b0\u30c7\u30fc\u30bf\u306b\u542b\u307e\u308c\u308b\u53ef\u80fd\u6027\u306e\u3042\u308b\u30d9\u30f3\u30c1\u30de\u30fc\u30af\u3060\u3051\u306b\u983c\u3089\u306a\u3044\u3053\u3068\u3002<\/p><\/li>
\u771f\u306e\u6c4e\u5316\u3092\u8a55\u4fa1\u3059\u308b\u305f\u3081\u306b\u3001\u3055\u307e\u3056\u307e\u306a\u554f\u984c\u306e\u30d0\u30ea\u30a8\u30fc\u30b7\u30e7\u30f3\u306b\u308f\u305f\u3063\u3066\u30d1\u30d5\u30a9\u30fc\u30de\u30f3\u30b9\u3092\u8a55\u4fa1\u3059\u308b\u3002<\/p><\/li>\n<\/ul>\n\n\n
CoT\u3078\u306e\u73fe\u5b9f\u7684\u306a\u671f\u5f85\uff1a<\/u><\/strong><\/p>\n\n\n\nCoT\u30d7\u30ed\u30f3\u30d7\u30c6\u30a3\u30f3\u30b0\u306f\u3001\u4e00\u822c\u5316\u306e\u9650\u754c\u3092\u7406\u89e3\u3057\u305f\u4e0a\u3067\u3001\u614e\u91cd\u306b\u4f7f\u7528\u3059\u308b\u3002<\/p><\/li>
CoT\u306b\u3088\u308b\u30d1\u30d5\u30a9\u30fc\u30de\u30f3\u30b9\u306e\u5411\u4e0a\u306f\u3001\u72ed\u3044\u554f\u984c\u30bb\u30c3\u30c8\u306b\u9650\u3089\u308c\u308b\u53ef\u80fd\u6027\u304c\u3042\u308b\u3053\u3068\u306b\u6ce8\u610f\u3057\u3066\u304f\u3060\u3055\u3044\u3002<\/p><\/li>
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LLM\u306e\u5f31\u70b9\u3067\u3042\u308b\u30a2\u30eb\u30b4\u30ea\u30ba\u30e0\u63a8\u8ad6\u3092\u88dc\u3044\u3064\u3064\u3001LLM\u306e\u9577\u6240\uff08\u81ea\u7136\u8a00\u8a9e\u7406\u89e3\u306a\u3069\uff09\u3092\u6d3b\u7528\u3067\u304d\u308b\u65b9\u6cd5\u3092\u63a2\u308b\u3002<\/p><\/li>\n<\/ul>\n\n\n
AI\u30a2\u30d7\u30ea\u30b1\u30fc\u30b7\u30e7\u30f3\u306b\u304a\u3051\u308b\u900f\u660e\u6027\uff1a<\/u><\/strong><\/p>\n\n\n\n\u7279\u306b\u63a8\u8ad6\u3084\u8a08\u753b\u4f5c\u696d\u3092\u4f34\u3046\u5834\u5408\u306b\u306f\u3001AI\u30b7\u30b9\u30c6\u30e0\u306e\u9650\u754c\u3092\u660e\u78ba\u306b\u4f1d\u3048\u308b\u3002<\/p><\/li>
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Chain-of-Thought (CoT) prompting has been hailed as a breakthrough in unlocking the reasoning capabilities of large language models (LLMs). This technique, which involves providing step-by-step reasoning examples to guide LLMs, has garnered significant attention in the AI community. Many researchers and practitioners have claimed that CoT prompting allows LLMs to tackle complex reasoning tasks more effectively, potentially bridging the gap between machine computation and human-like problem-solving. However, a recent paper titled “Chain of Thoughtlessness? An Analysis of CoT in Planning” challenges these optimistic claims. This research paper, focusing on planning tasks, provides a critical examination of CoT prompting’s effectiveness and generalizability. As AI practitioners, it’s crucial to understand these findings […]<\/p>\n","protected":false},"author":1003,"featured_media":13048,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"single-custom-post-template.php","format":"standard","meta":{"_et_pb_use_builder":"","_et_pb_old_content":"","_et_gb_content_width":"","footnotes":""},"categories":[100,67,119],"tags":[],"class_list":["post-12994","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-generative-ai","category-ml-nlp","category-prompt-engineering"],"yoast_head":"\n
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