Knowledge Graphs
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A knowledge graph is a structured representation of information that organizes data into nodes (entities) and edges (relationships) to show how different pieces of knowledge are interconnected. It enables enhanced data integration, search, and inference by modeling the relationships between concepts and entities in a graph format.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/luo-junyu/awesome-agent-papers
공식 코드Large Language Model Agent: A Survey on Methodology, Applications and Challenges
https://github.com/luomingshuang/m3gpt
공식 코드M$^3$GPT: An Advanced Multimodal, Multitask Framework for Motion Comprehension and Generation
https://github.com/luosx18/ued
공식 코드An Accurate Unsupervised Method for Joint Entity Alignment and Dangling Entity Detection
https://github.com/luozhongze/ForPKG
공식 코드ForPKG: A Framework for Constructing Forestry Policy Knowledge Graph and Application Analysis
https://github.com/lupantech/promptpg
공식 코드RetICL: Sequential Retrieval of In-Context Examples with Reinforcement Learning
https://github.com/lvxianwei/DeepMerge
공식 코드DeepMerge: Deep-Learning-Based Region-Merging for Image Segmentation
https://github.com/lxchtan/tegtok
공식 코드TegTok: Augmenting Text Generation via Task-specific and Open-world Knowledge
https://github.com/lycheeefish/vhexpansion
공식 코드Automatically Generating Visual Hallucination Test Cases for Multimodal Large Language Models
https://github.com/lyyf2002/asgea
공식 코드ASGEA: Exploiting Logic Rules from Align-Subgraphs for Entity Alignment
https://github.com/lzhang90/prompt/blob/main/prompts
공식 코드https://github.com/lzw-lzw/groundinggpt
공식 코드https://github.com/lzw-lzw/lego
공식 코드https://github.com/lzw-lzw/unifiedmllm
공식 코드UnifiedMLLM: Enabling Unified Representation for Multi-modal Multi-tasks With Large Language Model
https://github.com/lzw108/conspemollm
공식 코드ConspEmoLLM: Conspiracy Theory Detection Using an Emotion-Based Large Language Model
https://github.com/lzw108/raemollm
공식 코드https://github.com/lzxlin/mclea
공식 코드Multi-modal Contrastive Representation Learning for Entity Alignment
https://github.com/m-hildebrandt/R2D2
공식 코드https://github.com/m3hrdadfi/rag-memory-interplay
공식 코드https://github.com/m3rg-iitd/aila
공식 코드Autonomous Microscopy Experiments through Large Language Model Agents
https://github.com/ma787639046/tdro
공식 코드Task-level Distributionally Robust Optimization for Large Language Model-based Dense Retrieval
https://github.com/maarten-deraedt/idas-intent-discovery-with-abstract-summarization
공식 코드https://github.com/macaronlin/llama3-quantization
공식 코드An empirical study of LLaMA3 quantization: from LLMs to MLLMs
https://github.com/machine-reasoning-ufrgs/spixel-gat
공식 코드Superpixel Image Classification with Graph Attention Networks
https://github.com/machuangtao/kg-rag4sm
공식 코드Knowledge Graph-based Retrieval-Augmented Generation for Schema Matching
https://github.com/machuangtao/llm-kg4qa
공식 코드Large Language Models Meet Knowledge Graphs for Question Answering: Synthesis and Opportunities
https://github.com/maclab-hfut/psycollm
공식 코드PsycoLLM: Enhancing LLM for Psychological Understanding and Evaluation
https://github.com/magic-research/Sa2VA
공식 코드https://github.com/magkai/CONQUER
공식 코드https://github.com/mainuliitkgp/ar-bert
공식 코드https://github.com/maitrix-org/voila
공식 코드Voila: Voice-Language Foundation Models for Real-Time Autonomous Interaction and Voice Role-Play
https://github.com/malek-mechergui/codemdp
공식 코드Expectation Alignment: Handling Reward Misspecification in the Presence of Expectation Mismatch
https://github.com/mali-git/hyper_relational_ilp
공식 코드Improving Inductive Link Prediction Using Hyper-Relational Facts
https://github.com/malllabiisc/ConfGCN
공식 코드Neural Graph Embedding Methods for Natural Language Processing
https://github.com/malllabiisc/InteractE
공식 코드InteractE: Improving Convolution-based Knowledge Graph Embeddings by Increasing Feature Interactions
https://github.com/malllabiisc/cesi
공식 코드CESI: Canonicalizing Open Knowledge Bases using Embeddings and Side Information
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.