https://github.com/dair-iitd/kbi
공식 코드Joint Matrix-Tensor Factorization for Knowledge Base Inference
Papers with Code 아카이브
Knowledge Graph Completion
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- 7
- 보존된 평가 기록
- 17
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/dair-iitd/tkbi
공식 코드Temporal Knowledge Base Completion: New Algorithms and Evaluation Protocols
https://github.com/damo-nlp-sg/chain-of-knowledge
공식 코드https://github.com/damo-nlp-sg/llm-r2
공식 코드LLM-R2: A Large Language Model Enhanced Rule-based Rewrite System for Boosting Query Efficiency
https://github.com/dandoge/palm
공식 코드https://github.com/dangxingyu/rnn-icrag
공식 코드RNNs are not Transformers (Yet): The Key Bottleneck on In-context Retrieval
https://github.com/daniel-chin/flute-x-gpt
공식 코드https://github.com/daniel-gong/chatgpt-wm
공식 코드https://github.com/danielfperez/castillo
공식 코드CASTILLO: Characterizing Response Length Distributions of Large Language Models
https://github.com/danielkty/debiasing-rag
공식 코드https://github.com/daod/spring
공식 코드https://github.com/daromog/q-vid
공식 코드Question-Instructed Visual Descriptions for Zero-Shot Video Question Answering
https://github.com/darthreca/segmentation-losses-nlp
공식 코드Beyond Accuracy Optimization: Computer Vision Losses for Large Language Model Fine-Tuning
https://github.com/datarobot/syftr
공식 코드https://github.com/datascienceuibk/triviahg
공식 코드TriviaHG: A Dataset for Automatic Hint Generation from Factoid Questions
https://github.com/david-li0406/contextulization-distillation
공식 코드Contextualization Distillation from Large Language Model for Knowledge Graph Completion
https://github.com/davidheineman/category-fluency
공식 코드https://github.com/davidlamprecht/linkedpaperswithcode
공식 코드Linked Papers With Code: The Latest in Machine Learning as an RDF Knowledge Graph
https://github.com/davidlvxin/TransC
공식 코드Differentiating Concepts and Instances for Knowledge Graph Embedding
https://github.com/dayanayuan/raa-kgc
공식 코드Knowledge Graph Completion with Relation-Aware Anchor Enhancement
https://github.com/daynauth/elo_paper
공식 코드Ranking Unraveled: Recipes for LLM Rankings in Head-to-Head AI Combat
https://github.com/dcdmllm/align2llava
공식 코드https://github.com/dcdmllm/momentor
공식 코드Momentor: Advancing Video Large Language Model with Fine-Grained Temporal Reasoning
https://github.com/dcdmllm/worldgpt
공식 코드https://github.com/dchang56/snomed_kge
공식 코드Benchmark and Best Practices for Biomedical Knowledge Graph Embeddings
https://github.com/dcy0577/Text2BIM
공식 코드Text2BIM: Generating Building Models Using a Large Language Model-based Multi-Agent Framework
https://github.com/ddrrnn123/casc-ai
공식 코드CASC-AI: Consensus-aware Self-corrective AI Agents for Noise Cell Segmentation
https://github.com/de-tum/gnce
공식 코드Cardinality Estimation over Knowledge Graphs with Embeddings and Graph Neural Networks
https://github.com/debrup-61/mathsensei
공식 코드MATHSENSEI: A Tool-Augmented Large Language Model for Mathematical Reasoning
https://github.com/decibrain-group/dsgbench
공식 코드https://github.com/declare-lab/PromptDistill
공식 코드https://github.com/declare-lab/flacuna
공식 코드Flacuna: Unleashing the Problem Solving Power of Vicuna using FLAN Fine-Tuning
https://github.com/declare-lab/trust-align
공식 코드https://github.com/deep-polyu/awesome-graphrag
공식 코드A Survey of Graph Retrieval-Augmented Generation for Customized Large Language Models
https://github.com/deep-spin/lmt_hallucinations
공식 코드https://github.com/deep-spin/tower-eval
공식 코드Tower: An Open Multilingual Large Language Model for Translation-Related Tasks
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.