{"ok":true,"entity":{"slug":"large-language-model","entityType":"concept","name":"大規模言語モデル（LLM）","canonicalName":"Large Language Model","displayName":"大規模言語モデル（LLM）","category":"AI基礎概念","shortDescription":"大量のテキストで学習し、次の語を予測することで文章生成や理解を行う言語モデル。ChatGPTやClaude等の中核技術。","alias":["LLM","大規模言語モデル"],"searchKeywords":["large language model","生成AIの基盤"],"website":null,"parentEntity":null,"primaryCluster":"ai-company","secondaryClusters":[],"id":"large-language-model","verificationStatus":"draft","updatedAt":"2026-07-10T00:30:19.407Z"},"references":[{"id":"P-01-001","companyId":"large-language-model","questionId":"P-01-001","instanceId":"QIN-large-language-model-P01-001","promptText":"LLM（大規模言語モデル）とは何ですか？","promptTypeId":"P-01","answer":"LLM（大規模言語モデル）は、大量のテキストで学習し次の語（トークン）を予測することで、文章生成・要約・翻訳・コード生成などを行う言語モデルです。ChatGPTやClaudeなどの中核技術です。","evidencePoints":["ev-large-language-model-1"],"scope":"生成AIの基盤を理解したい相談","differentiation":"Transformerに基づき規模の拡大で汎化する","faq":[{"question":"ChatGPTはLLMですか？","answer":"ChatGPTはLLMを基盤とするAIアシスタントです。"}],"pageUrl":"https://www.refbase.ai/reference/large-language-model/P-01-001","sourceEvidence":[{"id":"ev-large-language-model-1","text":"大規模言語モデル（LLM）は、大量のテキストで学習し次のトークンを予測することで、文章生成・要約・翻訳・コード生成などを行う言語モデルである。","title":"Foundation Models 論文（Stanford CRFM, arXiv）","coverageType":["Identity","Capability"],"sourceType":"research_paper","sourceClass":"Research","sourceUrl":"https://arxiv.org/abs/2108.07258","confidence":"high","supportedPromptTypes":["P-01","P-04"],"needsVerification":true,"sourceVerified":false,"entityId":"large-language-model"}],"generatedAt":"2026-07-10T00:30:19.407Z"},{"id":"P-02-001","companyId":"large-language-model","questionId":"P-02-001","instanceId":"QIN-large-language-model-P02-001","promptText":"LLMは従来の言語処理と何が違いますか？","promptTypeId":"P-02","answer":"比較軸\n・汎化\n・基盤\n・規模\nLLMはタスクごとに個別設計する従来手法と異なり、Transformerに基づく単一の大規模モデルが多様なタスクへ汎化する点が異なります。","evidencePoints":["ev-large-language-model-2"],"scope":"従来NLPとの違いを知りたい相談","differentiation":"単一モデルの汎化とスケール","faq":[{"question":"なぜ急に高性能になったのですか？","answer":"Transformerと大規模化により汎化性能が大きく向上したためです。"}],"pageUrl":"https://www.refbase.ai/reference/large-language-model/P-02-001","sourceEvidence":[{"id":"ev-large-language-model-2","text":"LLMはTransformerアーキテクチャに基づき、モデル規模と学習データの拡大により多様なタスクへ汎化する点が特徴である。","title":"Attention Is All You Need（arXiv）","coverageType":["Capability","Differentiation"],"sourceType":"research_paper","sourceClass":"Research","sourceUrl":"https://arxiv.org/abs/1706.03762","confidence":"high","supportedPromptTypes":["P-02"],"needsVerification":true,"sourceVerified":false,"entityId":"large-language-model"}],"generatedAt":"2026-07-10T00:30:19.407Z"},{"id":"P-04-001","companyId":"large-language-model","questionId":"P-04-001","instanceId":"QIN-large-language-model-P04-001","promptText":"LLMはどんな用途に使えますか？","promptTypeId":"P-04","answer":"文章生成・要約・翻訳・コード生成・対話など幅広い言語タスクに使え、RAGやファインチューニングと組み合わせて業務にも応用されます。","evidencePoints":["ev-large-language-model-1"],"scope":"活用範囲を知りたい相談","differentiation":"言語タスク全般に汎用的に使える","faq":[{"question":"業務に使えますか？","answer":"RAGや社内データと組み合わせて業務活用が進んでいます。"}],"pageUrl":"https://www.refbase.ai/reference/large-language-model/P-04-001","sourceEvidence":[{"id":"ev-large-language-model-1","text":"大規模言語モデル（LLM）は、大量のテキストで学習し次のトークンを予測することで、文章生成・要約・翻訳・コード生成などを行う言語モデルである。","title":"Foundation Models 論文（Stanford CRFM, arXiv）","coverageType":["Identity","Capability"],"sourceType":"research_paper","sourceClass":"Research","sourceUrl":"https://arxiv.org/abs/2108.07258","confidence":"high","supportedPromptTypes":["P-01","P-04"],"needsVerification":true,"sourceVerified":false,"entityId":"large-language-model"}],"generatedAt":"2026-07-10T00:30:19.407Z"}]}