Papers with Code 아카이브
Knowledge Graph Completion
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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/git-disl/awesome-llm-game-agent-papers
공식 코드https://github.com/git-disl/gptlens
공식 코드Large Language Model-Powered Smart Contract Vulnerability Detection: New Perspectives
https://github.com/git-disl/safety-tax
공식 코드Safety Tax: Safety Alignment Makes Your Large Reasoning Models Less Reasonable
https://github.com/git-disl/vaccine
공식 코드Vaccine: Perturbation-aware Alignment for Large Language Models against Harmful Fine-tuning Attack
https://github.com/githubnext/testpilot
공식 코드An Empirical Evaluation of Using Large Language Models for Automated Unit Test Generation
https://github.com/gitter-lab/Assay2Mol
공식 코드Assay2Mol: large language model-based drug design using BioAssay context
https://github.com/gk0wk/xgraphrag
공식 코드XGraphRAG: Interactive Visual Analysis for Graph-based Retrieval-Augmented Generation
https://github.com/gkw0010/endochat
공식 코드EndoChat: Grounded Multimodal Large Language Model for Endoscopic Surgery
https://github.com/glaciohound/lerp
공식 코드Logical Entity Representation in Knowledge-Graphs for Differentiable Rule Learning
https://github.com/glowworm-cell/lict
공식 코드Objectively Evaluating the Reliability of Cell Type Annotation Using LLM-Based Strategies
https://github.com/gls0425/linvt
공식 코드LinVT: Empower Your Image-level Large Language Model to Understand Videos
https://github.com/gmaher/llmpc
공식 코드https://github.com/goliaro/specinfer-ae
공식 코드https://github.com/gomate-community/trustrag
공식 코드TrustRAG: An Information Assistant with Retrieval Augmented Generation
https://github.com/google-deepmind/loft
공식 코드Can Long-Context Language Models Subsume Retrieval, RAG, SQL, and More?
https://github.com/google-research-datasets/rag_conflicts
공식 코드DRAGged into Conflicts: Detecting and Addressing Conflicting Sources in Search-Augmented LLMs
https://github.com/google-research-datasets/swim-ir
공식 코드Leveraging LLMs for Synthesizing Training Data Across Many Languages in Multilingual Dense Retrieval
https://github.com/google-research/google-research
공식 코드Arithmetic Sampling: Parallel Diverse Decoding for Large Language Models
https://github.com/google-research/google-research/tree/master/table_rag
공식 코드TableRAG: Million-Token Table Understanding with Language Models
https://github.com/google-research/smore
공식 코드SMORE: Knowledge Graph Completion and Multi-hop Reasoning in Massive Knowledge Graphs
https://github.com/govtech-responsibleai/knowornot
공식 코드Know Or Not: a library for evaluating out-of-knowledge base robustness
https://github.com/gowitheflow-1998/rar-b
공식 코드https://github.com/gpt-laboratory/rag-llm-development-guidebook-from-pdfs
공식 코드Developing Retrieval Augmented Generation (RAG) based LLM Systems from PDFs: An Experience Report
https://github.com/gpt4vision/gpt4sgg-llm
공식 코드GPT4SGG: Synthesizing Scene Graphs from Holistic and Region-specific Narratives
https://github.com/gradiustwinbee/glis
공식 코드Global-Local Collaborative Inference with LLM for Lidar-Based Open-Vocabulary Detection
https://github.com/graph-and-geometric-learning/hypercore
공식 코드HyperCore: The Core Framework for Building Hyperbolic Foundation Models with Comprehensive Modules
https://github.com/graph-com/subgraphrag
공식 코드https://github.com/graphcore-research/kg-topology-toolbox
공식 코드The Role of Graph Topology in the Performance of Biomedical Knowledge Graph Completion Models
https://github.com/graphcore-research/llm-inference-research
공식 코드https://github.com/graphcore/distributed-kge-poplar
공식 코드BESS: Balanced Entity Sampling and Sharing for Large-Scale Knowledge Graph Completion
https://github.com/graphpku/coi
공식 코드https://github.com/graphpku/open-world-kg
공식 코드Rethinking Knowledge Graph Evaluation Under the Open-World Assumption
https://github.com/graphrag-bench/graphrag-benchmark
공식 코드When to use Graphs in RAG: A Comprehensive Analysis for Graph Retrieval-Augmented Generation
https://github.com/graphresearcher/autodata
공식 코드https://github.com/gregorycoppola/bayes-star
공식 코드The Quantified Boolean Bayesian Network: Theory and Experiments with a Logical Graphical Model
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