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/tigerchen52/pub_guard_llm
공식 코드Pub-Guard-LLM: Detecting Fraudulent Biomedical Articles with Reliable Explanations
https://github.com/tigerresearch/tigerbot
공식 코드SoftTiger: A Clinical Foundation Model for Healthcare Workflows
https://github.com/tigerwww-git/rt-kgd
공식 코드RT-KGD: Relation Transition Aware Knowledge-Grounded Dialogue Generation
https://github.com/tingchenfu/poisonbench
공식 코드PoisonBench: Assessing Large Language Model Vulnerability to Data Poisoning
https://github.com/tingjshen/urllm
공식 코드https://github.com/tisage/cybermentor
공식 코드https://github.com/tjtp/d2controller
공식 코드https://github.com/tjtp/kdar
공식 코드https://github.com/tjunlp-lab/awesome-llm-safety-papers
공식 코드https://github.com/tjunlp-lab/cmoraleval
공식 코드CMoralEval: A Moral Evaluation Benchmark for Chinese Large Language Models
https://github.com/tjunlp-lab/fuxitranyu
공식 코드FuxiTranyu: A Multilingual Large Language Model Trained with Balanced Data
https://github.com/tjzvbokbnft/ELITE-Embedding-Less-retrieval-with-Iterative-Text-Exploration
공식 코드ELITE: Embedding-Less retrieval with Iterative Text Exploration
https://github.com/tkenneweg/rag_dataset_gen
공식 코드https://github.com/tkipf/relational-gcn
공식 코드https://github.com/tmlr-group/deepinception
공식 코드DeepInception: Hypnotize Large Language Model to Be Jailbreaker
https://github.com/tmlr-group/one-shot-subgraph
공식 코드Less is More: One-shot Subgraph Reasoning on Large-scale Knowledge Graphs
https://github.com/tmlr-group/wca
공식 코드Visual-Text Cross Alignment: Refining the Similarity Score in Vision-Language Models
https://github.com/tobias-opsahl/fact-or-fiction
공식 코드https://github.com/tobischimanski/climretrieve
공식 코드ClimRetrieve: A Benchmarking Dataset for Information Retrieval from Corporate Climate Disclosures
https://github.com/tolusophy/Edge-of-Tomorrow
공식 코드On the Reliability of Large Language Models to Misinformed and Demographically-Informed Prompts
https://github.com/tom192180/Generate_then_Refine
공식 코드Generate then Refine: Data Augmentation for Zero-shot Intent Detection
https://github.com/tomchen-ctj/OST
공식 코드OST: Refining Text Knowledge with Optimal Spatio-Temporal Descriptor for General Video Recognition
https://github.com/tomsawyerhu/lrp4rag
공식 코드https://github.com/toneli/hnzslp
공식 코드A Hierarchical N-Gram Framework for Zero-Shot Link Prediction
https://github.com/toneli/petailor-for-bio-triple-extraction
공식 코드Benchingmaking Large Langage Models in Biomedical Triple Extraction
https://github.com/tongji-kgllm/rag-survey
공식 코드Retrieval-Augmented Generation for Large Language Models: A Survey
https://github.com/tongji-kgllm/u-niah
공식 코드U-NIAH: Unified RAG and LLM Evaluation for Long Context Needle-In-A-Haystack
https://github.com/tongjifinlab/cfbenchmark
공식 코드CFBenchmark: Chinese Financial Assistant Benchmark for Large Language Model
https://github.com/tongjifinlab/cfgpt
공식 코드CFGPT: Chinese Financial Assistant with Large Language Model
https://github.com/tongzhou21/CogMG
공식 코드CogMG: Collaborative Augmentation Between Large Language Model and Knowledge Graph
https://github.com/tongzhou21/oasis
공식 코드Oasis: Data Curation and Assessment System for Pretraining of Large Language Models
https://github.com/tonichopp/ecamp
공식 코드ECAMP: Entity-centered Context-aware Medical Vision Language Pre-training
https://github.com/tonychenxyz/selfie
공식 코드SelfIE: Self-Interpretation of Large Language Model Embeddings
https://github.com/tonyzhang617/nomad-dist
공식 코드NoMAD-Attention: Efficient LLM Inference on CPUs Through Multiply-add-free Attention
https://github.com/tool-lmm/tool-lmm
공식 코드MLLM-Tool: A Multimodal Large Language Model For Tool Agent Learning
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.