Papers with Code 아카이브
Knowledge Graph Completion
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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- 평가표
- 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/flyfree5/lahore
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공식 코드Protecting Privacy in Multimodal Large Language Models with MLLMU-Bench
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공식 코드Interpersonal Memory Matters: A New Task for Proactive Dialogue Utilizing Conversational History
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공식 코드XMainframe: A Large Language Model for Mainframe Modernization
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공식 코드DISC-FinLLM: A Chinese Financial Large Language Model based on Multiple Experts Fine-tuning
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공식 코드DISC-LawLLM: Fine-tuning Large Language Models for Intelligent Legal Services
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공식 코드Large Language Model-Based Agents for Software Engineering: A Survey
https://github.com/funaudiollm/cosyvoice
공식 코드https://github.com/funaudiollm/inspiremusic
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공식 코드Deep Natural Language Feature Learning for Interpretable Prediction
https://github.com/fusion-jena/automatic-kg-creation-with-llm
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https://github.com/futureeeeee/cognibench
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.