https://github.com/mberr/ea-sota-comparison
공식 코드A Critical Assessment of State-of-the-Art in Entity Alignment
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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.
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
A Critical Assessment of State-of-the-Art in Entity Alignment
On the Ambiguity of Rank-Based Evaluation of Entity Alignment or Link Prediction Methods
On-Demand and Lightweight Knowledge Graph Generation -- a Demonstration with DBpedia
FIRE: Fact-checking with Iterative Retrieval and Verification
XrayGPT: Chest Radiographs Summarization using Medical Vision-Language Models
MCCoder: Streamlining Motion Control with LLM-Assisted Code Generation and Rigorous Verification
Evaluating Dependencies in Fact Editing for Language Models: Specificity and Implication Awareness
MuLan: Multimodal-LLM Agent for Progressive and Interactive Multi-Object Diffusion
Combining Inductive and Deductive Reasoning for Query Answering over Incomplete Knowledge Graphs
CypherBench: Towards Precise Retrieval over Full-scale Modern Knowledge Graphs in the LLM Era
Distilling Large Language Models using Skill-Occupation Graph Context for HR-Related Tasks
Attention Instruction: Amplifying Attention in the Middle via Prompting
Asynchronous Large Language Model Enhanced Planner for Autonomous Driving
Geolocation of Cultural Heritage using Multi-View Knowledge Graph Embedding
Cross-modal RAG: Sub-dimensional Retrieval-Augmented Text-to-Image Generation
Beyond Scores: A Modular RAG-Based System for Automatic Short Answer Scoring with Feedback
Dubo-SQL: Diverse Retrieval-Augmented Generation and Fine Tuning for Text-to-SQL
Knowledge Graph Embedding for Link Prediction: A Comparative Analysis
VG-TVP: Multimodal Procedural Planning via Visually Grounded Text-Video Prompting
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.