https://github.com/TmacMai/CoMPILE_Inductive_Knowledge_Graph
공식 코드Communicative Message Passing for Inductive Relation Reasoning
Papers with Code 아카이브
Knowledge Graph Completion
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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- 4
- 평가표
- 7
- 보존된 평가 기록
- 17
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/TongLiu-github/TemperatureSaling4RTs
공식 코드https://github.com/TonyLianLong/LLM-groundedDiffusion
공식 코드https://github.com/TrustAIRLab/VoiceJailbreakAttack
공식 코드https://github.com/Trusted-AI/adversarial-robustness-toolbox
공식 코드Weakly Supervised Detection of Hallucinations in LLM Activations
https://github.com/Twilight92z/Role-of-LLM
공식 코드Understanding the Role of LLMs in Multimodal Evaluation Benchmarks
https://github.com/U-Alberta/KG-Type-Correction
공식 코드Typing Errors in Factual Knowledge Graphs: Severity and Possible Ways Out
https://github.com/UCSC-VLAA/MedReason
공식 코드MedReason: Eliciting Factual Medical Reasoning Steps in LLMs via Knowledge Graphs
https://github.com/UKPLab/acl2024-DARA
공식 코드https://github.com/UKPLab/kg2text
공식 코드Modeling Global and Local Node Contexts for Text Generation from Knowledge Graphs
https://github.com/UKPLab/plms-graph2text
공식 코드Investigating Pretrained Language Models for Graph-to-Text Generation
https://github.com/UMass-Foundation-Model/VisualCoT
공식 코드https://github.com/Unknown-Data/QGCN
공식 코드https://github.com/VARGPT-family/VARGPT
공식 코드https://github.com/VDIGPKU/OpenAD
공식 코드OpenAD: Open-World Autonomous Driving Benchmark for 3D Object Detection
https://github.com/VITA-MLLM/VITA
공식 코드https://github.com/Valdecy/pyDecision
공식 코드https://github.com/Valentyn1997/kg-alignment-lessons-learned
공식 코드Knowledge Graph Entity Alignment with Graph Convolutional Networks: Lessons Learned
https://github.com/VectorInstitute/biaskg
공식 코드BiasKG: Adversarial Knowledge Graphs to Induce Bias in Large Language Models
https://github.com/Vision-CAIR/MiniGPT4-video
공식 코드https://github.com/VulScribeR/VulScribeR
공식 코드VulScribeR: Exploring RAG-based Vulnerability Augmentation with LLMs
https://github.com/WPENGxs/X-WebAgentBench
공식 코드X-WebAgentBench: A Multilingual Interactive Web Benchmark for Evaluating Global Agentic System
https://github.com/WangCan1178/Plug-and-Play-Estimation
공식 코드Plug-and-Play Performance Estimation for LLM Services without Relying on Labeled Data
https://github.com/WangHanLinHenry/STeCa
공식 코드STeCa: Step-level Trajectory Calibration for LLM Agent Learning
https://github.com/WangXFng/ELMRec
공식 코드Enhancing High-order Interaction Awareness in LLM-based Recommender Model
https://github.com/WeiDongjunGabriel/ESA
공식 코드https://github.com/Weixin-Liang/Mapping-the-Increasing-Use-of-LLMs-in-Scientific-Papers
공식 코드https://github.com/Wentao-Xu/SEEK
공식 코드https://github.com/Whalefishin/LLM_animation
공식 코드Real-time Animation Generation and Control on Rigged Models via Large Language Models
https://github.com/WisdomShell/RewardAnything
공식 코드RewardAnything: Generalizable Principle-Following Reward Models
https://github.com/X-LANCE/SLAM-LLM
공식 코드DRCap: Decoding CLAP Latents with Retrieval-Augmented Generation for Zero-shot Audio Captioning
https://github.com/X-PLUG/mPLUG-Owl/tree/main/mPLUG-Owl2
공식 코드mPLUG-Owl2: Revolutionizing Multi-modal Large Language Model with Modality Collaboration
https://github.com/X1AOX1A/UniPoll
공식 코드UniPoll: A Unified Social Media Poll Generation Framework via Multi-Objective Optimization
https://github.com/XGenerationLab/XiYan-SQL
공식 코드A Preview of XiYan-SQL: A Multi-Generator Ensemble Framework for Text-to-SQL
https://github.com/XHMY/LLM-RankFusion
공식 코드LLM-RankFusion: Mitigating Intrinsic Inconsistency in LLM-based Ranking
https://github.com/XiaoduoAILab/XmodelLM-1.5
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.