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/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/sunflower-the-cat/DAEA
공식 코드https://github.com/sunipa/Abs-Orientation
공식 코드https://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/sunshy-1/HCMKR
공식 코드Hyperbolic Contrastive Learning with Model-augmentation for Knowledge-aware Recommendation
https://github.com/suprityoung/zhongjing
공식 코드https://github.com/susantiyuni/path-to-causality
공식 코드https://github.com/susavlsh10/polar-sparsity
공식 코드Polar Sparsity: High Throughput Batched LLM Inferencing with Scalable Contextual Sparsity
https://github.com/sushantgautam/factgenius
공식 코드https://github.com/sutaoyu/CDMEA
공식 코드Mitigating Modality Bias in Multi-modal Entity Alignment from a Causal Perspective
https://github.com/sutaoyu/IBMEA
공식 코드IBMEA: Exploring Variational Information Bottleneck for Multi-modal Entity Alignment
https://github.com/sutaoyu/LoginMEA
공식 코드LoginMEA: Local-to-Global Interaction Network for Multi-modal Entity Alignment
https://github.com/svakulenk0/KBQA
공식 코드Message Passing for Complex Question Answering over Knowledge Graphs
https://github.com/svjan5/kg-reeval
공식 코드https://github.com/swaggy66/M-ABSA
공식 코드M-ABSA: A Multilingual Dataset for Aspect-Based Sentiment Analysis
https://github.com/swagshaw/wilddesed
공식 코드WildDESED: An LLM-Powered Dataset for Wild Domestic Environment Sound Event Detection System
https://github.com/swathi-shree-narashiman/alphazip
공식 코드https://github.com/swordlidev/llava-vsd
공식 코드LLaVA-VSD: Large Language-and-Vision Assistant for Visual Spatial Description
https://github.com/sxswz213/crsec
공식 코드Emergence of Social Norms in Generative Agent Societies: Principles and Architecture
https://github.com/sy-shi/RLAIF_ScoreDiff
공식 코드Navigating Noisy Feedback: Enhancing Reinforcement Learning with Error-Prone Language Models
https://github.com/sybeam27/IAP-ZSAS
공식 코드Zero-Shot Industrial Anomaly Segmentation with Image-Aware Prompt Generation
https://github.com/sycny/chatgraph
공식 코드ChatGraph: Interpretable Text Classification by Converting ChatGPT Knowledge to Graphs
https://github.com/sydney-machine-learning/sentimentanalysis-bible
공식 코드Large language model for Bible sentiment analysis: Sermon on the Mount
https://github.com/synlp/chimed-gpt
공식 코드https://github.com/sysll/CovidLLM
공식 코드https://github.com/sysuselab/rustevo
공식 코드RustEvo^2: An Evolving Benchmark for API Evolution in LLM-based Rust Code Generation
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.