Papers with Code 아카이브
Knowledge Graphs
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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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/navidmdn/logic_based_qa
공식 코드https://github.com/nbalepur/mcqa-artifacts
공식 코드Is Your Large Language Model Knowledgeable or a Choices-Only Cheater?
https://github.com/ncbi-nlp/trialgpt
공식 코드Matching Patients to Clinical Trials with Large Language Models
https://github.com/ncclab-sustech/cocog
공식 코드CoCoG: Controllable Visual Stimuli Generation based on Human Concept Representations
https://github.com/ncsulsj/robust_sumsystem
공식 코드https://github.com/nddsg/KGMiner
공식 코드Discriminative Predicate Path Mining for Fact Checking in Knowledge Graphs
https://github.com/nec-research/emu-kg
공식 코드Optimal Embedding Guided Negative Sample Generation for Knowledge Graph Link Prediction
https://github.com/nec-research/fact-linking
공식 코드https://github.com/nec-research/recurrency_baseline_tkg
공식 코드History repeats Itself: A Baseline for Temporal Knowledge Graph Forecasting
https://github.com/neerajas-group/rumors-in-multi-agent
공식 코드Simulating Rumor Spreading in Social Networks using LLM Agents
https://github.com/neph0s/lmke
공식 코드https://github.com/nercms-mmap/friendsqa
공식 코드https://github.com/nervjack2/SpeechChatGPTStreaming
공식 코드Building a Taiwanese Mandarin Spoken Language Model: A First Attempt
https://github.com/netease-youdao/confucius3-math
공식 코드Confucius3-Math: A Lightweight High-Performance Reasoning LLM for Chinese K-12 Mathematics Learning
https://github.com/netop-team/telco-rag
공식 코드Telco-RAG: Navigating the Challenges of Retrieval-Augmented Language Models for Telecommunications
https://github.com/nets4geeks/dockercomposedataset
공식 코드https://github.com/networks-learning/counterfactual-llms
공식 코드https://github.com/networks-learning/coupled-llm-evaluation
공식 코드Evaluation of Large Language Models via Coupled Token Generation
https://github.com/networks-learning/prediction-powered-ranking
공식 코드https://github.com/netx-lab/ayo
공식 코드Teola: Towards End-to-End Optimization of LLM-based Applications
https://github.com/neuhai/uxagent
공식 코드UXAgent: An LLM Agent-Based Usability Testing Framework for Web Design
https://github.com/neuir/m2rag
공식 코드Benchmarking Retrieval-Augmented Generation in Multi-Modal Contexts
https://github.com/neuir/rankcot
공식 코드RankCoT: Refining Knowledge for Retrieval-Augmented Generation through Ranking Chain-of-Thoughts
https://github.com/neukg/techgpt-2.0
공식 코드TechGPT-2.0: A large language model project to solve the task of knowledge graph construction
https://github.com/neulab/ragged
공식 코드RAGGED: Towards Informed Design of Retrieval Augmented Generation Systems
https://github.com/neuralmind-ai/visconde
공식 코드https://github.com/neviim96/breakgpt
공식 코드BreakGPT: A Large Language Model with Multi-stage Structure for Financial Breakout Detection
https://github.com/newiz430/pretext
공식 코드A Survey on Self-Supervised Graph Foundation Models: Knowledge-Based Perspective
https://github.com/ngl567/GANA-FewShotKGC
공식 코드https://github.com/ngl567/cake
공식 코드CAKE: A Scalable Commonsense-Aware Framework For Multi-View Knowledge Graph Completion
https://github.com/ngl567/dhns
공식 코드Diffusion-based Hierarchical Negative Sampling for Multimodal Knowledge Graph Completion
https://github.com/ngl567/lcge
공식 코드Logic and Commonsense-Guided Temporal Knowledge Graph Completion
https://github.com/nhattlm95/tm_kg_dialogue
공식 코드Enhancing Knowledge Retrieval with Topic Modeling for Knowledge-Grounded Dialogue
https://github.com/nheist/Cat2Ax
공식 코드https://github.com/nheist/caligraph-for-semrec
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.