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  1. Home
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  3. /llmsresearch/llm-flashcards
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repoGitHubTrust 82 · PrimaryPublished 6d agoLive · 6d ago

llmsresearch/llm-flashcards

Visual knowledge bank for understanding large language models, with 180 concept cards from tokenization to deployment.

Lineage graph

Paper → model → repo connections mined from source citations (Tier-1 exact match).

Covers

newsIdentifying Interactions at Scale for LLMsnewsIEEE Rolls Out Large Language Models Virtual Training CoursenewsWhat's one local AI workflow you wish you'd discovered sooner?newsNew Server Hopes to Break Through AI’s “Memory Wall”newsBook Review: Domain-Specific Small Language Models by Guglielmo IozzianewsKnowledge Distillation of Black-Box Large Language Models

Implements

paperUnderstanding Large Language ModelspaperShow Me Examples: Inferring Visual Concepts from Image Sets

Implements (incoming)

paperTORINO: Token Reduction via Interpretable Concept Overlap in Vision-Language ModelspaperChatImage: Navigating Long-Form LLM Answers through Interactive ImagespaperSearch Beyond What Can Be Taught: Evolving the Knowledge Boundary in Agentic Visual GenerationpaperTILDE: TILt-based Distributional Erasure for Concept UnlearningpaperTowards Mechanistically Understanding Why Memorized Knowledge Fails to Generalize in Large Language Model FinetuningpaperVocaDet: Sample-Driven Open-Vocabulary Object Detection and Segmentation via Visual Tokenization and Vector Database RetrievalpaperWorkflow as Knowledge: Semantic Persistence for LLM-Mediated Workflows

Covers (incoming)

news[Paper] How much do language models memorize?newsThe Large Language Model (LLM) understands the world through human texts. A world model is needed f.. - 매일경제

Related across the graph

paperChatImage: Navigating Long-Form LLM Answers through Interactive Imagesnews[Paper] How much do language models memorize?newsKnowledge Distillation of Black-Box Large Language ModelspaperTILDE: TILt-based Distributional Erasure for Concept UnlearningnewsThe Large Language Model (LLM) understands the world through human texts. A world model is needed f.. - 매일경제paperTowards Mechanistically Understanding Why Memorized Knowledge Fails to Generalize in Large Language Model FinetuningpaperTORINO: Token Reduction via Interpretable Concept Overlap in Vision-Language ModelsnewsNew Server Hopes to Break Through AI’s “Memory Wall”paperVocaDet: Sample-Driven Open-Vocabulary Object Detection and Segmentation via Visual Tokenization and Vector Database RetrievalnewsBook Review: Domain-Specific Small Language Models by Guglielmo IozziapaperUnderstanding Large Language ModelspaperWorkflow as Knowledge: Semantic Persistence for LLM-Mediated WorkflowsnewsIEEE Rolls Out Large Language Models Virtual Training CoursenewsWhat's one local AI workflow you wish you'd discovered sooner?paperShow Me Examples: Inferring Visual Concepts from Image SetspaperSearch Beyond What Can Be Taught: Evolving the Knowledge Boundary in Agentic Visual GenerationnewsIdentifying Interactions at Scale for LLMs
Knowledge path·PChatImage: Navigating Long-Form LLM Answers through Interactive Images→N[Paper] How much do language models memorize?→NKnowledge Distillation of Black-Box Large Language Models→Rllmsresearch/llm-flashcards

Topics

agentsaiankiattentiondeep-learningfine-tuningflashcardsgptinterview-preparationlarge-language-models

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Graph trust82Primary
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