Papers with Code 아카이브

Knowledge Graphs

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

전체 기법 전체 데이터셋

하위 태스크: 5
평가표: 4
보존된 평가 기록: 19

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.

코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/asahi417/relbert

공식 코드

Distilling Relation Embeddings from Pre-trained Language Models

https://github.com/asappresearch/halugen

공식 코드

Enhancing Hallucination Detection through Perturbation-Based Synthetic Data Generation in System Responses

https://github.com/asappresearch/josh-llm-simulation-training

공식 코드

Sparse Rewards Can Self-Train Dialogue Agents

https://github.com/ascherp/semantic-web-primer

공식 코드

Semantic Web: Past, Present, and Future

https://github.com/asgaardlab/LLMxBugs

공식 코드

Large Language Models are Pretty Good Zero-Shot Video Game Bug Detectors

https://github.com/ash-ai-safety-hub/g3-nandi

공식 코드

Comparing Optimization Targets for Contrast-Consistent Search

https://github.com/ashfarhangi/Protoformer

공식 코드

Protoformer: Embedding Prototypes for Transformers

https://github.com/asinghcsu/agenticrag-survey

공식 코드

Agentic Retrieval-Augmented Generation: A Survey on Agentic RAG

https://github.com/asoulet/superficiality

공식 코드

The Structure and Dynamics of Knowledge Graphs, with Superficiality

https://github.com/assassinws/llm-tamp

공식 코드

LLM3:Large Language Model-based Task and Motion Planning with Motion Failure Reasoning

https://github.com/astral-group/bdc_mitigation_assessment

공식 코드

The Emperor's New Clothes in Benchmarking? A Rigorous Examination of Mitigation Strategies for LLM Benchmark Data Contamination

https://github.com/astrazeneca/awesome-drug-discovery-knowledge-graphs

공식 코드

A Review of Biomedical Datasets Relating to Drug Discovery: A Knowledge Graph Perspective

https://github.com/astrazeneca/biomedical-kg-topological-imbalance

공식 코드

Implications of Topological Imbalance for Representation Learning on Biomedical Knowledge Graphs

https://github.com/astrazeneca/kgem-in-drug-discovery

공식 코드

Understanding the Performance of Knowledge Graph Embeddings in Drug Discovery

https://github.com/astrazeneca/onto_merger

공식 코드

OntoMerger: An Ontology Integration Library for Deduplicating and Connecting Knowledge Graph Nodes

https://github.com/asu-kim/personal-llm-kg

공식 코드

Personalizing Large Language Models using Retrieval Augmented Generation and Knowledge Graph

https://github.com/atenanaz/poison-rag

공식 코드

Poison-RAG: Adversarial Data Poisoning Attacks on Retrieval-Augmented Generation in Recommender Systems

https://github.com/augustinLib/SPIKE

공식 코드

Imagine All The Relevance: Scenario-Profiled Indexing with Knowledge Expansion for Dense Retrieval

https://github.com/autextification/AuTexTification-Overview

공식 코드

Overview of AuTexTification at IberLEF 2023: Detection and Attribution of Machine-Generated Text in Multiple Domains

https://github.com/auto-icd-coding/ruccod

공식 코드

RuCCoD: Towards Automated ICD Coding in Russian

https://github.com/avdpro/ai2apps

공식 코드

AI2Apps: A Visual IDE for Building LLM-based AI Agent Applications

https://github.com/avinashsai/ViRED

공식 코드

Learning to Retrieve Videos by Asking Questions

https://github.com/awslabs/graphrag-toolkit

공식 코드

Hierarchical Lexical Graph for Enhanced Multi-Hop Retrieval

https://github.com/awslabs/hlat

공식 코드

HLAT: High-quality Large Language Model Pre-trained on AWS Trainium

https://github.com/awslabs/optimizing-multitask-training-through-dynamic-pipelines

공식 코드

DynaPipe: Optimizing Multi-task Training through Dynamic Pipelines

https://github.com/awslabs/rag-qa-arena

공식 코드

RAG-QA Arena: Evaluating Domain Robustness for Long-form Retrieval Augmented Question Answering

https://github.com/ayushraina10/sgts

공식 코드

Learning to design without prior data: Discovering generalizable design strategies using deep learning and tree search

https://github.com/azminewasi/banglaautokg

공식 코드

BanglaAutoKG: Automatic Bangla Knowledge Graph Construction with Semantic Neural Graph Filtering

https://github.com/azminewasi/hrgraph

공식 코드

HRGraph: Leveraging LLMs for HR Data Knowledge Graphs with Information Propagation-based Job Recommendation

https://github.com/azreasoners/llm-asp

공식 코드

Coupling Large Language Models with Logic Programming for Robust and General Reasoning from Text

https://github.com/baai-dcai/m3d

공식 코드

M3D: Advancing 3D Medical Image Analysis with Multi-Modal Large Language Models

https://github.com/babahui/progressive-text-to-image

공식 코드

Progressive Text-to-Image Diffusion with Soft Latent Direction

https://github.com/baharefatemi/ReAlE

공식 코드

Knowledge Hypergraph Embedding Meets Relational Algebra

https://github.com/bahuia/hgnet

공식 코드

Outlining and Filling: Hierarchical Query Graph Generation for Answering Complex Questions over Knowledge Graphs

https://github.com/bai-lab/emotionalrag

공식 코드

Emotional RAG: Enhancing Role-Playing Agents through Emotional Retrieval

https://github.com/bai-lab/kg-retriever

공식 코드

KG-Retriever: Efficient Knowledge Indexing for Retrieval-Augmented Large Language Models

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.