https://github.com/fs302/EasyLink/blob/main/example/ogbl_ppa_ra.py
Neural Bellman-Ford Networks: A General Graph Neural Network Framework for Link Prediction
Papers with Code 아카이브
Knowledge Graphs
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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- 평가표
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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/fudandisc/reform-eval
MME: A Comprehensive Evaluation Benchmark for Multimodal Large Language Models
https://github.com/fumiyo0607/KGAN
https://github.com/funaudiollm/cv3-eval
CosyVoice 3: Towards In-the-wild Speech Generation via Scaling-up and Post-training
https://github.com/gair-nlp/abel
Solving Quantitative Reasoning Problems with Language Models
https://github.com/gengchenmai/se-kge
Contextual Graph Attention for Answering Logical Queries over Incomplete Knowledge Graphs
https://github.com/giacoballoccu/rep-path-reasoning-recsys
Reinforcement Recommendation Reasoning through Knowledge Graphs for Explanation Path Quality
https://github.com/giannispana/ails-ntua-at-semeval-2024-task-9-brainteaser
BRAINTEASER: Lateral Thinking Puzzles for Large Language Models
https://github.com/giantonia/KGATv2
https://github.com/gicheonkang/gst-visdial
https://github.com/giuseppefutia/semi
https://github.com/goodevening13/aquakv
Pushing the Limits of Large Language Model Quantization via the Linearity Theorem
https://github.com/goodmike31/pl-asr-bigos-tools
https://github.com/google-deepmind/alphadev
https://github.com/graphcore-research/bess-kge
BESS: Balanced Entity Sampling and Sharing for Large-Scale Knowledge Graph Completion
https://github.com/graphcore/ogb-lsc-pcqm4mv2
OGB-LSC: A Large-Scale Challenge for Machine Learning on Graphs
https://github.com/greenbitai/low_bit_llama
OmniQuant: Omnidirectionally Calibrated Quantization for Large Language Models
https://github.com/guillaumejaume/tuto-dl-on-graphs
https://github.com/guixiangyu1/KGE-HAKE-master
Learning Hierarchy-Aware Knowledge Graph Embeddings for Link Prediction
https://github.com/gusye1234/nano-graphrag
From Local to Global: A Graph RAG Approach to Query-Focused Summarization
https://github.com/gzhu06/cacophony
https://github.com/hahnyuan/llmviewer
https://github.com/haiquanlu/alphapruning
Eigenspectrum Analysis of Neural Networks without Aspect Ratio Bias
https://github.com/hanguo97/flute
Pushing the Limits of Large Language Model Quantization via the Linearity Theorem
https://github.com/hannaabiakl/psychic
PSYCHIC: A Neuro-Symbolic Framework for Knowledge Graph Question-Answering Grounding
https://github.com/hazyresearch/flash-attention
Efficient Memory Management for Large Language Model Serving with PagedAttention
https://github.com/hazyresearch/legalbench
Learning to Generate Instruction Tuning Datasets for Zero-Shot Task Adaptation
https://github.com/heegyukim/open-korean-instructions
Prometheus: Inducing Fine-grained Evaluation Capability in Language Models
https://github.com/hemingkx/Spec-Bench
https://github.com/heyuan9/medscore
https://github.com/hieuphan33/cvpr2024_mavl
https://github.com/hitz-zentroa/eval-MCG-COLING-2025
A LLM-Based Ranking Method for the Evaluation of Automatic Counter-Narrative Generation
https://github.com/hitz-zentroa/lm-contamination
NLP Evaluation in trouble: On the Need to Measure LLM Data Contamination for each Benchmark
https://github.com/hitz-zentroa/whisper-lm-transformers
Whisper-LM: Improving ASR Models with Language Models for Low-Resource Languages
https://github.com/hiyouga/easyr1
https://github.com/hkuds/auto-deep-research
AutoAgent: A Fully-Automated and Zero-Code Framework for LLM Agents
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.