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AI\u7814\u7a76\u306e\u6700\u524d\u7dda\u306b\u3042\u308b\u4e00\u6b21\u60c5\u5831\u306b\u89e6\u308c\u3089\u308c\u308b<\/h3>\n\n\n\n

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\u3069\u306e\u3088\u3046\u306a\u30d7\u30ed\u30f3\u30d7\u30c8\u3092\u4e0e\u3048\u308b\u304b\u306b\u3088\u3063\u3066\u3001LLM\u304b\u3089\u5f15\u304d\u51fa\u3055\u308c\u308b\u30a2\u30a6\u30c8\u30d7\u30c3\u30c8\u306e\u8cea\u306f\u5287\u7684\u306b\u5909\u5316\u3057\u307e\u3059\u3002<\/p>\n\n\n\n

\u8cea\u306e\u9ad8\u3044\u30d7\u30ed\u30f3\u30d7\u30c8\u306fLLM\u306e\u6f5c\u5728\u80fd\u529b\u3092\u6700\u5927\u9650\u306b\u5f15\u304d\u51fa\u3057\u3001\u4e00\u65b9\u3067\u8cea\u306e\u4f4e\u3044\u30d7\u30ed\u30f3\u30d7\u30c8\u306f\u610f\u56f3\u3057\u306a\u3044\u3001\u3042\u308b\u3044\u306f\u4e0d\u6b63\u78ba\u306a\u56de\u7b54\u3092\u751f\u6210\u3055\u305b\u3066\u3057\u307e\u3046\u306e\u3067\u3059\u3002<\/p>\n\n\n\n

\u30d7\u30ed\u30f3\u30d7\u30c8\u30a8\u30f3\u30b8\u30cb\u30a2\u30ea\u30f3\u30b0\u306e\u76ee\u7684\u3068\u91cd\u8981\u6027<\/h3>\n\n\n\n

\u30d7\u30ed\u30f3\u30d7\u30c8\u30a8\u30f3\u30b8\u30cb\u30a2\u30ea\u30f3\u30b0\u3068\u306f\u3001LLM\u304b\u3089\u671b\u307e\u3057\u3044\u51fa\u529b\u3092\u5f97\u308b\u305f\u3081\u306b\u3001\u6700\u9069\u306a\u30d7\u30ed\u30f3\u30d7\u30c8\u3092\u8a2d\u8a08\u3001\u69cb\u7bc9\u3001\u6700\u9069\u5316\u3059\u308b\u4e00\u9023\u306e\u6280\u8853\u3084\u5b66\u554f\u5206\u91ce\u3092\u6307\u3057\u307e\u3059\u3002<\/strong><\/p>\n\n\n\n

\u305d\u306e\u76ee\u7684\u306f\u3001LLM\u306e\u6027\u80fd\u3092\u6700\u5927\u9650\u306b\u5f15\u304d\u51fa\u3057\u3001\u7279\u5b9a\u306e\u30bf\u30b9\u30af\u3092\u52b9\u7387\u7684\u304b\u3064\u9ad8\u7cbe\u5ea6\u306b\u5b9f\u884c\u3055\u305b\u308b\u3053\u3068\u306b\u3042\u308a\u307e\u3059\u3002<\/p>\n\n\n\n

LLM\u306e\u6027\u80fd\u306f\u56fa\u5b9a\u7684\u3067\u306f\u306a\u304f\u3001\u30d7\u30ed\u30f3\u30d7\u30c8\u3068\u3044\u3046\u300c\u9375\u300d\u306b\u3088\u3063\u3066\u305d\u306e\u80fd\u529b\u304c\u5f15\u304d\u51fa\u3055\u308c\u307e\u3059\u3002<\/p>\n\n\n\n

\u305d\u306e\u305f\u3081\u3001\u30d7\u30ed\u30f3\u30d7\u30c8\u30a8\u30f3\u30b8\u30cb\u30a2\u30ea\u30f3\u30b0\u306f\u3001AI\u6d3b\u7528\u306e\u6210\u5426\u3092\u5206\u3051\u308b\u6975\u3081\u3066\u91cd\u8981\u306a\u30b9\u30ad\u30eb\u3068\u306a\u3063\u3066\u3044\u307e\u3059\u3002<\/p>\n\n\n\n

\u4f8b\u3048\u3070\u3001\u540c\u3058LLM\u3092\u4f7f\u3063\u3066\u3082\u3001\u30d7\u30ed\u30f3\u30d7\u30c8\u306e\u5de5\u592b\u6b21\u7b2c\u3067\u3001\u3042\u308a\u304d\u305f\u308a\u306a\u6587\u7ae0\u3057\u304b\u66f8\u3051\u306a\u3044\u72b6\u614b\u304b\u3089\u3001\u30d7\u30ed\u306e\u30e9\u30a4\u30bf\u30fc\u306e\u3088\u3046\u306a\u8aac\u5f97\u529b\u306e\u3042\u308b\u6587\u7ae0\u3092\u751f\u6210\u3067\u304d\u308b\u72b6\u614b\u3078\u3068\u5909\u5316\u3055\u305b\u308b\u3053\u3068\u304c\u53ef\u80fd\u306a\u306e\u3067\u3059\u3002<\/p>\n\n\n\n

\u3010\u53b3\u907835\u9078\u3011\u30d7\u30ed\u30f3\u30d7\u30c8\u30a8\u30f3\u30b8\u30cb\u30a2\u30ea\u30f3\u30b0\u306e\u5fc5\u8aad\u8ad6\u6587\u30ea\u30b9\u30c8<\/h2>\n\n\n\n

\u3053\u3053\u3067\u306f\u3001\u30d7\u30ed\u30f3\u30d7\u30c8\u30a8\u30f3\u30b8\u30cb\u30a2\u30ea\u30f3\u30b0\u3092\u5b66\u3076\u4e0a\u3067\u5fc5\u8aad\u3068\u8a00\u3048\u308b\u8ad6\u6587\u309235\u672c\u3001\u53b3\u9078\u3057\u3066\u7d39\u4ecb\u3057\u307e\u3059\u3002<\/p>\n\n\n\n

\u307e\u305a\u306f\u57fa\u672c\u3068\u3057\u3066\u7d76\u5bfe\u306b\u62bc\u3055\u3048\u3066\u304a\u304d\u305f\u304410\u672c\u3001\u6b21\u306b\u3001\u3088\u308a\u6df1\u3044\u77e5\u8b58\u3092\u5f97\u308b\u305f\u3081\u306e\u5fdc\u7528\u30fb\u767a\u5c55\u7684\u306a25\u672c\u306b\u5206\u3051\u3066\u30ea\u30b9\u30c8\u30a2\u30c3\u30d7\u3057\u307e\u3057\u305f\u3002<\/p>\n\n\n\n

\u3010\u307e\u305a\u8aad\u3080\u3079\u304d\u3011\u57fa\u672c\u3068\u306a\u308b\u91cd\u8981\u8ad6\u658710\u9078<\/h3>\n\n\n\n

\n
1. Language Models are Few-Shot Learners<\/a><\/li>\n\n\n\n
2. Chain-of-Thought Prompting Elicits Reasoning in Large Language Models<\/a><\/li>\n\n\n\n
3. SELF-CONSISTENCY IMPROVES CHAIN OF THOUGHT REASONING IN LANGUAGE MODELS<\/a><\/li>\n\n\n\n
4. Tree of Thoughts: Deliberate Problem Solving with Large Language Models<\/a><\/li>\n\n\n\n
5. Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks<\/a><\/li>\n\n\n\n
6. Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods<\/a><\/li>\n\n\n\n
7. Emergent Abilities of Large Language Models<\/a><\/li>\n\n\n\n
8. A Survey of Large Language Models<\/a><\/li>\n\n\n\n
9. Reasoning with Language Model Prompting: A Survey<\/a><\/li>\n\n\n\n
10. Augmented Language Models: a Survey<\/a><\/li>\n<\/ol>\n\n\n\n

    \u3010\u6280\u8853\u3092\u6df1\u3081\u308b\u3011\u5fdc\u7528\u30fb\u767a\u5c55\u8ad6\u658725\u9078<\/h3>\n\n\n\n

    \n
    1. FINETUNED LANGUAGE MODELS ARE ZERO-SHOT LEARNERS<\/a><\/li>\n\n\n\n
    2. Deep Reinforcement Learning from Human Preferences<\/a><\/li>\n\n\n\n
    3. LLaMA: Open and Efficient Foundation Language Models<\/a><\/li>\n\n\n\n
    4. Scaling Laws for Neural Language Models<\/a><\/li>\n\n\n\n
    5. Rethinking the Role of Demonstrations: What Makes In-Context Learning Work?<\/a><\/li>\n\n\n\n
    6. Large Language Models are Zero-Shot Reasoners<\/a><\/li>\n\n\n\n
    7. AUTOMATIC CHAIN OF THOUGHT PROMPTING IN LARGE LANGUAGE MODELS<\/a><\/li>\n\n\n\n
    8. Generated Knowledge Prompting for Commonsense Reasoning<\/a><\/li>\n\n\n\n
    9. Large Language Model Guided Tree-of-Thought<\/a><\/li>\n\n\n\n
    10. ART: Automatic multi-step reasoning and tool-use for large language models<\/a><\/li>\n\n\n\n
    11. LARGE LANGUAGE MODELS ARE HUMAN-LEVEL PROMPT ENGINEERS<\/a><\/li>\n\n\n\n
    12. Active Prompting with Chain-of-Thought for Large Language Models<\/a><\/li>\n\n\n\n
    13. Guiding Large Language Models via Directional Stimulus Prompting<\/a><\/li>\n\n\n\n
    14. REACT: SYNERGIZING REASONING AND ACTING IN LANGUAGE MODELS<\/a><\/li>\n\n\n\n
    15. Multimodal Chain-of-Thought Reasoning in Language Models<\/a><\/li>\n\n\n\n
    16. A Taxonomy of Prompt Modifiers for Text-To-Image Generation<\/a><\/li>\n\n\n\n
    17. Towards Reasoning in Large Language Models: A Survey<\/a><\/li>\n\n\n\n
    18. A Survey for In-context Learning<\/a><\/li>\n\n\n\n
    19. Nature Language Reasoning, A Survey<\/a><\/li>\n\n\n\n
    20. A Bibliometric Review of Large Language Models Research from 2017 to 2023<\/a><\/li>\n\n\n\n
    21. One Small Step for Generative AI, One Giant Leap for AGI: A Complete Survey on ChatGPT in AIGC Era<\/a><\/li>\n\n\n\n
    22. Tool Learning with Foundation Models<\/a><\/li>\n\n\n\n
    23. Harnessing the Power of LLMs in Practice: A Survey on ChatGPT and Beyond<\/a><\/li>\n\n\n\n
    24. Jailbreaking ChatGPT via Prompt Engineering: An Empirical Study<\/a><\/li>\n\n\n\n
    25. Few-shot Fine-tuning vs. In-context Learning: A Fair Comparison and Evaluation<\/a><\/li>\n<\/ol>\n\n\n\n

        \u8ad6\u6587\u304b\u3089\u5b66\u3076\uff01\u30d7\u30ed\u30f3\u30d7\u30c8\u30a8\u30f3\u30b8\u30cb\u30a2\u30ea\u30f3\u30b0\u306e\u4e3b\u8981\u6280\u8853<\/h2>\n\n\n\n

        \u6570\u591a\u304f\u306e\u8ad6\u6587\u3067\u63d0\u6848\u3055\u308c\u3066\u304d\u305f\u30d7\u30ed\u30f3\u30d7\u30c8\u30a8\u30f3\u30b8\u30cb\u30a2\u30ea\u30f3\u30b0\u306e\u6280\u8853\u306e\u4e2d\u3067\u3082\u3001\u7279\u306b\u91cd\u8981\u3067\u57fa\u790e\u3068\u306a\u308b\u3082\u306e\u30925\u3064\u7d39\u4ecb\u3057\u307e\u3059\u3002<\/p>\n\n\n\n

        \u3053\u308c\u3089\u306e\u6280\u8853\u306e\u30b3\u30f3\u30bb\u30d7\u30c8\u3092\u7406\u89e3\u3059\u308b\u3053\u3068\u3067\u3001\u591a\u304f\u306e\u5fdc\u7528\u8ad6\u6587\u306e\u8aad\u89e3\u304c\u30b9\u30e0\u30fc\u30ba\u306b\u306a\u308a\u3001\u5b9f\u8df5\u3067\u3082\u5f79\u7acb\u3066\u308b\u3053\u3068\u304c\u3067\u304d\u307e\u3059\u3002<\/p>\n\n\n\n

        \u3010\u57fa\u672c\u3011Zero-shot \/ Few-shot \u30d7\u30ed\u30f3\u30d7\u30c6\u30a3\u30f3\u30b0<\/h3>\n\n\n\n

        Zero-shot\u30d7\u30ed\u30f3\u30d7\u30c6\u30a3\u30f3\u30b0\u306f\u3001\u30bf\u30b9\u30af\u306e\u4f8b\u3092\u4e00\u5207\u793a\u3055\u305a\u306b\u3001\u6307\u793a\u3060\u3051\u3067LLM\u306b\u30bf\u30b9\u30af\u3092\u89e3\u304b\u305b\u308b\u624b\u6cd5\u3067\u3059\u3002<\/strong><\/p>\n\n\n\n

        \u4f8b\u3048\u3070\u3001\u300c\u6b21\u306e\u6587\u7ae0\u3092\u8981\u7d04\u3057\u3066\u304f\u3060\u3055\u3044\uff1a…\u300d\u306e\u3088\u3046\u306b\u3001\u76f4\u63a5\u7684\u306a\u547d\u4ee4\u3092\u4e0e\u3048\u307e\u3059\u3002<\/p>\n\n\n\n

        \u4e00\u65b9\u3001Few-shot\u30d7\u30ed\u30f3\u30d7\u30c6\u30a3\u30f3\u30b0\u306f\u3001\u30d7\u30ed\u30f3\u30d7\u30c8\u5185\u306b\u3044\u304f\u3064\u304b\u306e\u4f8b\uff08\u5165\u529b\u3068\u51fa\u529b\u306e\u30da\u30a2\uff09\u3092\u63d0\u793a\u3059\u308b\u3053\u3068\u3067\u3001LLM\u304c\u30bf\u30b9\u30af\u306e\u610f\u56f3\u3092\u3088\u308a\u6b63\u78ba\u306b\u7406\u89e3\u3067\u304d\u308b\u3088\u3046\u306b\u4fc3\u3059\u624b\u6cd5\u3067\u3059\u3002<\/p>\n\n\n\n

        GPT-3\u306e\u6027\u80fd\u3092\u98db\u8e8d\u7684\u306b\u77e5\u3089\u3057\u3081\u305f\u8ad6\u6587\u300cLanguage Models are Few-Shot Learners\u300d\u3067\u305d\u306e\u6709\u52b9\u6027\u304c\u793a\u3055\u308c\u3001\u30d7\u30ed\u30f3\u30d7\u30c8\u30a8\u30f3\u30b8\u30cb\u30a2\u30ea\u30f3\u30b0\u306e\u57fa\u672c\u4e2d\u306e\u57fa\u672c\u3068\u3057\u3066\u4f4d\u7f6e\u3065\u3051\u3089\u308c\u3066\u3044\u307e\u3059\u3002<\/p>\n\n\n\n

        Few-shot\u30d7\u30ed\u30f3\u30d7\u30c6\u30a3\u30f3\u30b0\u306f\u3001\u30e2\u30c7\u30eb\u306b\u8ffd\u52a0\u306e\u5b66\u7fd2\uff08\u30d5\u30a1\u30a4\u30f3\u30c1\u30e5\u30fc\u30cb\u30f3\u30b0\uff09\u3092\u65bd\u3059\u3053\u3068\u306a\u304f\u3001\u6587\u8108\u5185\u3067\u30bf\u30b9\u30af\u3092\u5b66\u7fd2\u3055\u305b\u308b\u300c\u30a4\u30f3\u30b3\u30f3\u30c6\u30ad\u30b9\u30c8\u5b66\u7fd2\u300d\u306e\u57fa\u790e\u3068\u306a\u308b\u8003\u3048\u65b9\u3067\u3059\u3002<\/p>\n\n\n\n

        \u3010\u63a8\u8ad6\u80fd\u529b\u5411\u4e0a\u3011CoT (Chain-of-Thought)<\/h3>\n\n\n\n

        CoT (Chain-of-Thought)\u306f\u3001LLM\u306b\u6700\u7d42\u7684\u306a\u7b54\u3048\u3060\u3051\u3067\u306a\u304f\u3001\u305d\u3053\u306b\u81f3\u308b\u307e\u3067\u306e\u4e2d\u9593\u7684\u306a\u601d\u8003\u306e\u9023\u9396\uff08Chain of Thought\uff09\u3092\u751f\u6210\u3055\u305b\u308b\u624b\u6cd5\u3067\u3059\u3002<\/strong><\/p>\n\n\n\n

        \u7279\u306b\u3001\u7b97\u8853\u554f\u984c\u3084\u5e38\u8b58\u7684\u63a8\u8ad6\u306a\u3069\u3001\u8907\u6570\u306e\u30b9\u30c6\u30c3\u30d7\u3092\u7d4c\u3066\u7d50\u8ad6\u306b\u81f3\u308b\u3088\u3046\u306a\u8907\u96d1\u306a\u30bf\u30b9\u30af\u306b\u304a\u3044\u3066\u3001LLM\u306e\u63a8\u8ad6\u80fd\u529b\u3092\u5927\u5e45\u306b\u5411\u4e0a\u3055\u305b\u308b\u3053\u3068\u304c\u77e5\u3089\u308c\u3066\u3044\u307e\u3059\u3002<\/p>\n\n\n\n

        \u30d7\u30ed\u30f3\u30d7\u30c8\u306b\u300c\u30b9\u30c6\u30c3\u30d7\u30d0\u30a4\u30b9\u30c6\u30c3\u30d7\u3067\u8003\u3048\u3066\u304f\u3060\u3055\u3044\u300d\u3068\u3044\u3063\u305f\u4e00\u6587\u3092\u52a0\u3048\u308b\u3060\u3051\u3067\u3001LLM\u306f\u601d\u8003\u30d7\u30ed\u30bb\u30b9\u3092\u6587\u7ae0\u5316\u3057\u306a\u304c\u3089\u56de\u7b54\u3092\u5c0e\u304d\u51fa\u3059\u3088\u3046\u306b\u306a\u308a\u307e\u3059\u3002<\/p>\n\n\n\n

        \u3053\u308c\u306b\u3088\u308a\u3001\u5358\u306b\u7b54\u3048\u3092\u51fa\u3059\u3060\u3051\u3067\u306a\u304f\u3001\u305d\u306e\u30d7\u30ed\u30bb\u30b9\u304c\u6b63\u3057\u3044\u304b\u3069\u3046\u304b\u3092\u4eba\u9593\u304c\u691c\u8a3c\u3057\u3084\u3059\u304f\u306a\u308b\u3068\u3044\u3046\u30e1\u30ea\u30c3\u30c8\u3082\u3042\u308a\u307e\u3059\u3002<\/p>\n\n\n\n

        \u3053\u306e\u6280\u8853\u306f\u3001\u300cChain-of-Thought Prompting Elicits Reasoning in Large Language Models\u300d\u3068\u3044\u3046\u8ad6\u6587\u3067\u63d0\u6848\u3055\u308c\u307e\u3057\u305f\u3002<\/p>\n\n\n\n

        \u3053\u3061\u3089\u306f\u3001Chain-of-Thought\u30d7\u30ed\u30f3\u30d7\u30c6\u30a3\u30f3\u30b0\u306e\u5177\u4f53\u7684\u306a\u4f7f\u3044\u65b9\u3084\u30b3\u30c4\u3092\u3001\u8c4a\u5bcc\u306a\u4f8b\u3068\u5171\u306b\u89e3\u8aac\u3057\u305f\u8a18\u4e8b\u3067\u3059\u3002\u3042\u308f\u305b\u3066\u3054\u89a7\u304f\u3060\u3055\u3044\u3002 https:\/\/www.promptingguide.ai\/jp\/techniques\/cot<\/a><\/p>\n\n\n\n

        <\/figure>\n\n\n\n

        \u3010\u56de\u7b54\u7cbe\u5ea6\u5411\u4e0a\u3011Self-Consistency<\/h3>\n\n\n\n

        Self-Consistency\u306f\u3001CoT\u3092\u3055\u3089\u306b\u767a\u5c55\u3055\u305b\u305f\u624b\u6cd5\u3067\u3001\u56de\u7b54\u306e\u7cbe\u5ea6\u3068\u4fe1\u983c\u6027\u3092\u9ad8\u3081\u308b\u3053\u3068\u3092\u76ee\u7684\u3068\u3057\u307e\u3059\u3002<\/strong><\/p>\n\n\n\n

        \u3053\u306e\u30a2\u30d7\u30ed\u30fc\u30c1\u3067\u306f\u3001\u307e\u305a\u540c\u3058\u554f\u984c\u306b\u5bfe\u3057\u3066CoT\u3092\u7528\u3044\u3066\u3001\u8907\u6570\u306e\u7570\u306a\u308b\u601d\u8003\u30d7\u30ed\u30bb\u30b9\u3068\u56de\u7b54\u3092\u751f\u6210\u3055\u305b\u307e\u3059\u3002<\/p>\n\n\n\n

        LLM\u306f\u601d\u8003\u306e\u7d4c\u8def\u304c\u591a\u69d8\u3067\u3042\u308b\u305f\u3081\u3001\u540c\u3058\u6307\u793a\u3067\u3082\u6bce\u56de\u5c11\u3057\u305a\u3064\u9055\u3046\u30a2\u30a6\u30c8\u30d7\u30c3\u30c8\u3092\u51fa\u3057\u307e\u3059\u3002<\/p>\n\n\n\n

        \u305d\u3057\u3066\u3001\u751f\u6210\u3055\u308c\u305f\u8907\u6570\u306e\u56de\u7b54\u306e\u4e2d\u304b\u3089\u3001\u591a\u6570\u6c7a\u306e\u539f\u7406\u3067\u6700\u3082\u4e00\u8cab\u6027\u306e\u3042\u308b\uff08\u6700\u3082\u591a\u304f\u51fa\u73fe\u3057\u305f\uff09\u7b54\u3048\u3092\u6700\u7d42\u7684\u306a\u56de\u7b54\u3068\u3057\u3066\u63a1\u7528\u3057\u307e\u3059\u3002<\/p>\n\n\n\n

        \u3053\u308c\u306b\u3088\u308a\u3001\u5358\u4e00\u306e\u601d\u8003\u30d7\u30ed\u30bb\u30b9\u3067\u5076\u7136\u751f\u3058\u305f\u8aa4\u308a\u3092\u6392\u9664\u3057\u3001\u3088\u308a\u9811\u5065\u3067\u4fe1\u983c\u6027\u306e\u9ad8\u3044\u7d50\u679c\u3092\u5f97\u308b\u3053\u3068\u304c\u53ef\u80fd\u306b\u306a\u308a\u307e\u3059\u3002<\/p>\n\n\n\n

        \u300cSELF-CONSISTENCY IMPROVES CHAIN OF THOUGHT REASONING IN LANGUAGE MODELS\u300d\u3067\u305d\u306e\u8a73\u7d30\u304c\u8ff0\u3079\u3089\u308c\u3066\u3044\u307e\u3059\u3002<\/p>\n\n\n\n

        \u3010\u8907\u96d1\u306a\u554f\u984c\u89e3\u6c7a\u3011Tree of Thoughts (ToT)<\/h3>\n\n\n\n

        Tree of Thoughts (ToT)\u306f\u3001CoT\u304c\u4e00\u672c\u9053\u306e\u76f4\u7dda\u7684\u306a\u601d\u8003\u30d7\u30ed\u30bb\u30b9\u3067\u3042\u308b\u306e\u306b\u5bfe\u3057\u3001\u601d\u8003\u3092\u6728\u69cb\u9020\uff08\u30c4\u30ea\u30fc\u72b6\uff09\u306b\u5206\u5c90\u3055\u305b\u306a\u304c\u3089\u9032\u3081\u308b\u3001\u3088\u308a\u9ad8\u5ea6\u306a\u554f\u984c\u89e3\u6c7a\u624b\u6cd5\u3067\u3059\u3002<\/strong><\/p>\n\n\n\n

        \u4eba\u9593\u306e\u601d\u8003\u30d7\u30ed\u30bb\u30b9\u304c\u3001\u3042\u308b\u6642\u70b9\u3067\u8907\u6570\u306e\u9078\u629e\u80a2\u3092\u8003\u3048\u3001\u305d\u308c\u305e\u308c\u3092\u8a55\u4fa1\u3057\u3001\u6709\u671b\u306a\u65b9\u5411\u306b\u9032\u3080\u3053\u3068\u306b\u7740\u60f3\u3092\u5f97\u3066\u3044\u307e\u3059\u3002<\/p>\n\n\n\n

        ToT\u3067\u306f\u3001LLM\u306f\u601d\u8003\u306e\u5404\u30b9\u30c6\u30c3\u30d7\u3067\u8907\u6570\u306e\u53ef\u80fd\u6027\u3092\u751f\u6210\u3057\u3001\u305d\u308c\u3089\u306e\u6709\u52b9\u6027\u3092\u81ea\u5df1\u8a55\u4fa1\u3057\u3001\u6709\u671b\u306a\u601d\u8003\u306e\u300c\u679d\u300d\u3092\u4f38\u3070\u3057\u3066\u3044\u304d\u307e\u3059\u3002<\/p>\n\n\n\n

        \u3053\u306e\u63a2\u7d22\u7684\u306a\u30d7\u30ed\u30bb\u30b9\u306b\u3088\u308a\u3001CoT\u3067\u306f\u89e3\u304f\u3053\u3068\u304c\u96e3\u3057\u3044\u3001\u8a08\u753b\u7acb\u6848\u3084\u6226\u7565\u7684\u601d\u8003\u3092\u8981\u3059\u308b\u8907\u96d1\u306a\u554f\u984c\u306b\u3082\u5bfe\u5fdc\u3067\u304d\u308b\u3088\u3046\u306b\u306a\u308a\u307e\u3059\u3002<\/p>\n\n\n\n

        \u300cTree of Thoughts: Deliberate Problem Solving with Large Language Models\u300d\u3067\u63d0\u6848\u3055\u308c\u3001LLM\u306e\u81ea\u5f8b\u7684\u306a\u554f\u984c\u89e3\u6c7a\u80fd\u529b\u3092\u65b0\u305f\u306a\u30ec\u30d9\u30eb\u306b\u5f15\u304d\u4e0a\u3052\u307e\u3057\u305f\u3002<\/p>\n\n\n\n

        \u3010\u5916\u90e8\u77e5\u8b58\u306e\u6d3b\u7528\u3011Retrieval-Augmented Generation (RAG)<\/h3>\n\n\n\n

        Retrieval-Augmented Generation (RAG)\u306f\u3001LLM\u304c\u5143\u3005\u6301\u3063\u3066\u3044\u308b\u5185\u90e8\u77e5\u8b58\u306e\u9650\u754c\u3092\u514b\u670d\u3059\u308b\u305f\u3081\u306e\u6280\u8853\u3067\u3059\u3002<\/strong><\/p>\n\n\n\n

        LLM\u306e\u77e5\u8b58\u306f\u5b66\u7fd2\u30c7\u30fc\u30bf\u306b\u542b\u307e\u308c\u308b\u60c5\u5831\u306b\u9650\u5b9a\u3055\u308c\u308b\u305f\u3081\u3001\u6700\u65b0\u306e\u60c5\u5831\u3084\u3001\u7279\u5b9a\u306e\u4f01\u696d\u5185\u30c9\u30ad\u30e5\u30e1\u30f3\u30c8\u306e\u3088\u3046\u306a\u975e\u516c\u958b\u60c5\u5831\u306b\u306f\u30a2\u30af\u30bb\u30b9\u3067\u304d\u307e\u305b\u3093\u3002<\/p>\n\n\n\n

        RAG\u306f\u3001\u30e6\u30fc\u30b6\u30fc\u304b\u3089\u306e\u8cea\u554f\u306b\u95a2\u9023\u3059\u308b\u60c5\u5831\u3092\u3001\u307e\u305a\u5916\u90e8\u306e\u30c7\u30fc\u30bf\u30d9\u30fc\u30b9\u3084\u6587\u66f8\u7fa4\u304b\u3089\u691c\u7d22\uff08Retrieval\uff09\u3057\u3001\u5f97\u3089\u308c\u305f\u60c5\u5831\u3092\u30d7\u30ed\u30f3\u30d7\u30c8\u306b\u57cb\u3081\u8fbc\u3093\u3067LLM\u306b\u6e21\u3057\u307e\u3059\u3002<\/p>\n\n\n\n

        LLM\u306f\u305d\u306e\u60c5\u5831\u3092\u3082\u3068\u306b\u56de\u7b54\u3092\u751f\u6210\uff08Generation\uff09\u3057\u307e\u3059\u3002<\/p>\n\n\n\n

        \u3053\u308c\u306b\u3088\u308a\u3001LLM\u306f\u6700\u65b0\u304b\u3064\u6b63\u78ba\u306a\u60c5\u5831\u306b\u57fa\u3065\u3044\u305f\u56de\u7b54\u3092\u751f\u6210\u3067\u304d\u308b\u3088\u3046\u306b\u306a\u308a\u3001\u8aa4\u3063\u305f\u60c5\u5831\u3092\u751f\u6210\u3059\u308b\u300c\u30cf\u30eb\u30b7\u30cd\u30fc\u30b7\u30e7\u30f3\u300d\u3092\u5927\u5e45\u306b\u6291\u5236\u3059\u308b\u3053\u3068\u304c\u3067\u304d\u307e\u3059\u3002<\/p>\n\n\n\n

        \u3053\u3061\u3089\u306f\u3001RAG\u3067\u6d3b\u7528\u3067\u304d\u308b\u5177\u4f53\u7684\u306a\u30d7\u30ed\u30f3\u30d7\u30c8\u306e\u4f5c\u6210\u65b9\u6cd5\u3092\u89e3\u8aac\u3057\u305f\u8a18\u4e8b\u3067\u3059\u3002\u3042\u308f\u305b\u3066\u3054\u89a7\u304f\u3060\u3055\u3044\u3002<\/p>\n\n\n\n

        \n

        RAG\u3092\u6d3b\u7528\u3057\u305f\u52b9\u679c\u7684\u306a\u30d7\u30ed\u30f3\u30d7\u30c85\u9078<\/a><\/blockquote>