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/shaojintian/power_law_decay
공식 코드https://github.com/shaoxiongji/awesome-knowledge-graph
공식 코드A Survey on Knowledge Graphs: Representation, Acquisition and Applications
https://github.com/shark-nlp/openicl
공식 코드https://github.com/sharonlevy/contextualquestions
공식 코드https://github.com/shawnhuang497/bird
공식 코드A Refer-and-Ground Multimodal Large Language Model for Biomedicine
https://github.com/shawnhuang497/medplib
공식 코드Towards a Multimodal Large Language Model with Pixel-Level Insight for Biomedicine
https://github.com/shawnkx/Decom
공식 코드Decompressing Knowledge Graph Representations for Link Prediction
https://github.com/shawnye2000/creagent
공식 코드https://github.com/shayankiyani98/cp
공식 코드https://github.com/shayantalaei/chess
공식 코드https://github.com/sheikhshafayat/multi-fact
공식 코드Multi-FAct: Assessing Factuality of Multilingual LLMs using FActScore
https://github.com/shengwei-tian/dependency-parser-visualization
공식 코드AutoLLM-CARD: Towards a Description and Landscape of Large Language Models
https://github.com/shentianxiao/film
공식 코드https://github.com/shenyanghuang/tgb
공식 코드TGB 2.0: A Benchmark for Learning on Temporal Knowledge Graphs and Heterogeneous Graphs
https://github.com/shijiawenwen/judgedeceiver
공식 코드Optimization-based Prompt Injection Attack to LLM-as-a-Judge
https://github.com/shiluohe/referencetrustabledecoding
공식 코드Reference Trustable Decoding: A Training-Free Augmentation Paradigm for Large Language Models
https://github.com/shiralkarprashant/knowledgestream
공식 코드Finding Streams in Knowledge Graphs to Support Fact Checking
https://github.com/shoron-dutta/MALOnt-1
공식 코드https://github.com/shoron-dutta/cblip
공식 코드Replacing Paths with Connection-Biased Attention for Knowledge Graph Completion
https://github.com/showlab/videolisa
공식 코드One Token to Seg Them All: Language Instructed Reasoning Segmentation in Videos
https://github.com/shrango/poss
공식 코드POSS: Position Specialist Generates Better Draft for Speculative Decoding
https://github.com/shtuplus/iccc_cvpr2024
공식 코드Learning by Correction: Efficient Tuning Task for Zero-Shot Generative Vision-Language Reasoning
https://github.com/shubhamagarwal92/litllm
공식 코드https://github.com/shumkashun/first
공식 코드FIRST: Teach A Reliable Large Language Model Through Efficient Trustworthy Distillation
https://github.com/shuoli90/traq
공식 코드TRAQ: Trustworthy Retrieval Augmented Question Answering via Conformal Prediction
https://github.com/shuwen-liu-ox/loginfer
공식 코드Revisiting Inferential Benchmarks for Knowledge Graph Completion
https://github.com/si0wang/visvm
공식 코드Scaling Inference-Time Search with Vision Value Model for Improved Visual Comprehension
https://github.com/sib-swiss/sparql-llm
공식 코드LLM-based SPARQL Query Generation from Natural Language over Federated Knowledge Graphs
https://github.com/sichunluo/rallrec_plus
공식 코드RALLRec+: Retrieval Augmented Large Language Model Recommendation with Reasoning
https://github.com/sichunluo/recranker
공식 코드RecRanker: Instruction Tuning Large Language Model as Ranker for Top-k Recommendation
https://github.com/siddharthl93/design_kgex
공식 코드Retrieval Augmented Generation using Engineering Design Knowledge
https://github.com/siddharthl93/engineering-design-knowledge
공식 코드Retrieval Augmented Generation using Engineering Design Knowledge
https://github.com/sienna-wxy/kdgene
공식 코드Knowledge Graph Completion based on Tensor Decomposition for Disease Gene Prediction
https://github.com/sierra-research/tau2-bench
공식 코드$τ^2$-Bench: Evaluating Conversational Agents in a Dual-Control Environment
https://github.com/silin159/comfact
공식 코드ComFact: A Benchmark for Linking Contextual Commonsense Knowledge
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.