https://github.com/applied-machine-learning-lab/llmemb
공식 코드LLMEmb: Large Language Model Can Be a Good Embedding Generator for Sequential Recommendation
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/applied-machine-learning-lab/mill
공식 코드MILL: Mutual Verification with Large Language Models for Zero-Shot Query Expansion
https://github.com/april-tools/gekcs
공식 코드How to Turn Your Knowledge Graph Embeddings into Generative Models
https://github.com/araag2/SemEval2024-Task2
공식 코드https://github.com/archersama/uni-ctr
공식 코드A Unified Framework for Multi-Domain CTR Prediction via Large Language Models
https://github.com/archiki/utgendebug
공식 코드https://github.com/arghadippurdue/GraNNite
공식 코드https://github.com/arminarj/empathic-grounding
공식 코드https://github.com/armitakhn/gilbert
공식 코드https://github.com/artessay/sama
공식 코드TC-RAG:Turing-Complete RAG's Case study on Medical LLM Systems
https://github.com/arthur422tp/hierachical
공식 코드https://github.com/arthurleom/dress-llm
공식 코드DRESSing Up LLM: Efficient Stylized Question-Answering via Style Subspace Editing
https://github.com/artificial-scientist-lab/scimuse
공식 코드https://github.com/arturzolkowski/steganographic-evals
공식 코드https://github.com/arvid-pku/atoke
공식 코드History Matters: Temporal Knowledge Editing in Large Language Model
https://github.com/aryan-jadon/synthetic-data-generation-and-evaluation-using-reasoning-models
공식 코드https://github.com/aryopg/biokge
공식 코드https://github.com/asappresearch/halugen
공식 코드https://github.com/asappresearch/josh-llm-simulation-training
공식 코드https://github.com/asgaardlab/LLMxBugs
공식 코드Large Language Models are Pretty Good Zero-Shot Video Game Bug Detectors
https://github.com/ash-ai-safety-hub/g3-nandi
공식 코드Comparing Optimization Targets for Contrast-Consistent Search
https://github.com/ashfarhangi/Protoformer
공식 코드https://github.com/asinghcsu/agenticrag-survey
공식 코드Agentic Retrieval-Augmented Generation: A Survey on Agentic RAG
https://github.com/assassinws/llm-tamp
공식 코드LLM3:Large Language Model-based Task and Motion Planning with Motion Failure Reasoning
https://github.com/astral-group/bdc_mitigation_assessment
공식 코드https://github.com/astrazeneca/biomedical-kg-topological-imbalance
공식 코드Implications of Topological Imbalance for Representation Learning on Biomedical Knowledge Graphs
https://github.com/astrazeneca/kgem-in-drug-discovery
공식 코드Understanding the Performance of Knowledge Graph Embeddings in Drug Discovery
https://github.com/asu-kim/personal-llm-kg
공식 코드Personalizing Large Language Models using Retrieval Augmented Generation and Knowledge Graph
https://github.com/atenanaz/poison-rag
공식 코드https://github.com/augustinLib/SPIKE
공식 코드Imagine All The Relevance: Scenario-Profiled Indexing with Knowledge Expansion for Dense Retrieval
https://github.com/autextification/AuTexTification-Overview
공식 코드https://github.com/auto-icd-coding/ruccod
공식 코드https://github.com/avdpro/ai2apps
공식 코드AI2Apps: A Visual IDE for Building LLM-based AI Agent Applications
https://github.com/avinashsai/ViRED
공식 코드https://github.com/awslabs/graphrag-toolkit
공식 코드https://github.com/awslabs/hlat
공식 코드HLAT: High-quality Large Language Model Pre-trained on AWS Trainium
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.