Papers with Code 아카이브
Knowledge Graph Completion
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 4
- 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/parallelcodefoundry/ParEval
공식 코드https://github.com/parameterlab/trap
공식 코드TRAP: Targeted Random Adversarial Prompt Honeypot for Black-Box Identification
https://github.com/pardissz/translate-with-care
공식 코드https://github.com/particlemedia/ragtruth
공식 코드RAGTruth: A Hallucination Corpus for Developing Trustworthy Retrieval-Augmented Language Models
https://github.com/pasquini-dario/LLMmap
공식 코드https://github.com/pasquini-dario/llm_neuralexec
공식 코드Neural Exec: Learning (and Learning from) Execution Triggers for Prompt Injection Attacks
https://github.com/passing2961/thanos
공식 코드Thanos: Enhancing Conversational Agents with Skill-of-Mind-Infused Large Language Model
https://github.com/pat-jj/KARE
공식 코드Reasoning-Enhanced Healthcare Predictions with Knowledge Graph Community Retrieval
https://github.com/pat-jj/Retrieval-And-Structure
공식 코드RAS: Retrieval-And-Structuring for Knowledge-Intensive LLM Generation
https://github.com/pat-jj/ras
공식 코드RAS: Retrieval-And-Structuring for Knowledge-Intensive LLM Generation
https://github.com/pat-jj/s3
공식 코드s3: You Don't Need That Much Data to Train a Search Agent via RL
https://github.com/pat-jj/tagreal
공식 코드Text-Augmented Open Knowledge Graph Completion via Pre-Trained Language Models
https://github.com/paul-rottger/safetyprompts-paper
공식 코드https://github.com/paul-sheridan/paper-heaps-law-llm
공식 코드https://github.com/pauldrm/targeted_intervention
공식 코드HSI: Head-Specific Intervention Can Induce Misaligned AI Coordination in Large Language Models
https://github.com/paulgavrikov/vlm_shapebias
공식 코드https://github.com/paxnea/genAI-rngt
공식 코드A Comparison of Large Language Model and Human Performance on Random Number Generation Tasks
https://github.com/pbaghershahi/TGCN
공식 코드Efficient Relation-aware Neighborhood Aggregation in Graph Neural Networks via Tensor Decomposition
https://github.com/pbihao/slm
공식 코드https://github.com/pdx97/sbi-rag_neurips2024
공식 코드https://github.com/pedrocurvo/thetraitors
공식 코드The Traitors: Deception and Trust in Multi-Agent Language Model Simulations
https://github.com/peijiez/intentrec4maps
공식 코드Intention Recognition in Real-Time Interactive Navigation Maps
https://github.com/pengboci/graphrag-survey
공식 코드https://github.com/pengshuai-rin/multimath
공식 코드MultiMath: Bridging Visual and Mathematical Reasoning for Large Language Models
https://github.com/pengxiao-song/lawgpt
공식 코드LawGPT: A Chinese Legal Knowledge-Enhanced Large Language Model
https://github.com/perceptioncomputinglab/medfilip
공식 코드https://github.com/personnoname/fgeodrl
공식 코드FGeo-DRL: Deductive Reasoning for Geometric Problems through Deep Reinforcement Learning
https://github.com/perubhardwaj/attributionattack
공식 코드Adversarial Attacks on Knowledge Graph Embeddings via Instance Attribution Methods
https://github.com/perubhardwaj/inferenceattack
공식 코드Poisoning Knowledge Graph Embeddings via Relation Inference Patterns
https://github.com/petergriffinjin/search-r1
공식 코드Search-R1: Training LLMs to Reason and Leverage Search Engines with Reinforcement Learning
https://github.com/petrosrapto/pakton
공식 코드PAKTON: A Multi-Agent Framework for Question Answering in Long Legal Agreements
https://github.com/peyman-bi/sattle
공식 코드Self-attention Presents Low-dimensional Knowledge Graph Embeddings for Link Prediction
https://github.com/pgg3/L-AutoDA
공식 코드L-AutoDA: Leveraging Large Language Models for Automated Decision-based Adversarial Attacks
https://github.com/phellonchen/DMRM
공식 코드DMRM: A Dual-channel Multi-hop Reasoning Model for Visual Dialog
https://github.com/phelps-sg/llm-cooperation
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.