Papers with Code 아카이브
Knowledge Graph Completion
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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- 7
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Knowledge graphs $G$ are represented as a collection of triples $\\{(h, r, t)\\}\subseteq E\times R\times E$, where $E$ and $R$ are the entity set and relation set. The task of Knowledge Graph Completion is to either predict unseen relations $r$ between two existing entities: $(h, ?, t)$ or predict the tail entity $t$ given the head entity and the query relation: $(h, r, ?)$.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/ssm123ssm/docgpt-pharm
공식 코드https://github.com/st143575/SemPlaus-plausibleparrots
공식 코드https://github.com/stanford-futuredata/ares
공식 코드ARES: An Automated Evaluation Framework for Retrieval-Augmented Generation Systems
https://github.com/stanford-oval/wikichat
공식 코드https://github.com/stardust-coder/japanese-lm-med-harness
공식 코드https://github.com/starmpcc/asclepius
공식 코드Publicly Shareable Clinical Large Language Model Built on Synthetic Clinical Notes
https://github.com/starsuzi/adaptive-rag
공식 코드https://github.com/starsuzi/videorag
공식 코드https://github.com/start2020/awesome-timeseries-llm-fm
공식 코드https://github.com/stasl0217/KEnS
공식 코드Multilingual Knowledge Graph Completion via Ensemble Knowledge Transfer
https://github.com/stavc/unleashingworms-extractingdata
공식 코드https://github.com/stefanpie/hls-eval
공식 코드HLS-Eval: A Benchmark and Framework for Evaluating LLMs on High-Level Synthesis Design Tasks
https://github.com/stephen-smj/llamacare
공식 코드LlamaCare: A Large Medical Language Model for Enhancing Healthcare Knowledge Sharing
https://github.com/stephenturner/biorecap
공식 코드biorecap: an R package for summarizing bioRxiv preprints with a local LLM
https://github.com/stephlong614/causal-disco
공식 코드https://github.com/stevengrove/gpt4tools
공식 코드GPT4Tools: Teaching Large Language Model to Use Tools via Self-instruction
https://github.com/stevenjamesmoore/learningatscale24
공식 코드Automated Generation and Tagging of Knowledge Components from Multiple-Choice Questions
https://github.com/stochasticai/xturing
공식 코드https://github.com/stream-bench/stream-bench
공식 코드StreamBench: Towards Benchmarking Continuous Improvement of Language Agents
https://github.com/striderdu/spa
공식 코드Search to Pass Messages for Temporal Knowledge Graph Completion
https://github.com/strong-ai-lab/explanation-generation
공식 코드https://github.com/strong-ai-lab/logical-equivalence-driven-amr-data-augmentation-for-representation-learning
공식 코드Abstract Meaning Representation-Based Logic-Driven Data Augmentation for Logical Reasoning
https://github.com/studyzx/gesturegpt_iss
공식 코드GestureGPT: Toward Zero-Shot Free-Form Hand Gesture Understanding with Large Language Model Agents
https://github.com/suamin/LowFER
공식 코드https://github.com/suamin/umls-medline-distant-re
공식 코드A Data-driven Approach for Noise Reduction in Distantly Supervised Biomedical Relation Extraction
https://github.com/sufe-aiflm-lab/fin-r1
공식 코드Fin-R1: A Large Language Model for Financial Reasoning through Reinforcement Learning
https://github.com/sufianj/def_extraction
공식 코드Extracting Definienda in Mathematical Scholarly Articles with Transformers
https://github.com/sugarandgugu/GaVaMoE
공식 코드GaVaMoE: Gaussian-Variational Gated Mixture of Experts for Explainable Recommendation
https://github.com/suletekkesinoglu/xai_llm_rag
공식 코드From Feature Importance to Natural Language Explanations Using LLMs with RAG
https://github.com/sunblaze-ucb/cybergym
공식 코드CyberGym: Evaluating AI Agents' Cybersecurity Capabilities with Real-World Vulnerabilities at Scale
https://github.com/sunnynexus/retrollm
공식 코드RetroLLM: Empowering Large Language Models to Retrieve Fine-grained Evidence within Generation
https://github.com/sunnynexus/search-o1
공식 코드https://github.com/suprityoung/zhongjing
공식 코드https://github.com/susavlsh10/polar-sparsity
공식 코드Polar Sparsity: High Throughput Batched LLM Inferencing with Scalable Contextual Sparsity
https://github.com/svjan5/kg-reeval
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.