Knowledge Graphs
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 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/oneal2000/judge
공식 코드JuDGE: Benchmarking Judgment Document Generation for Chinese Legal System
https://github.com/oneal2000/prag
공식 코드https://github.com/onixhoque/sptransx-mlsys2025-reproduce
공식 코드https://github.com/open-compass/CriticBench
공식 코드https://github.com/open-compass/criticeval
공식 코드https://github.com/open-social-world/egonormia
공식 코드https://github.com/openai/human-eval
공식 코드Safurai 001: New Qualitative Approach for Code LLM Evaluation
https://github.com/openai/whisper
공식 코드Adapting Large Language Model with Speech for Fully Formatted End-to-End Speech Recognition
https://github.com/openaudiolab/llast
공식 코드LLaST: Improved End-to-end Speech Translation System Leveraged by Large Language Models
https://github.com/openbgbenchmark/openbg
공식 코드Construction and Applications of Billion-Scale Pre-Trained Multimodal Business Knowledge Graph
https://github.com/openbiolink/thoughtsource
공식 코드ThoughtSource: A central hub for large language model reasoning data
https://github.com/openbmb/chatdev
공식 코드Scaling Large Language Model-based Multi-Agent Collaboration
https://github.com/openbmb/minicpm
공식 코드https://github.com/openbmb/rageval
공식 코드RAGEval: Scenario Specific RAG Evaluation Dataset Generation Framework
https://github.com/openbmb/repoagent
공식 코드RepoAgent: An LLM-Powered Open-Source Framework for Repository-level Code Documentation Generation
https://github.com/openbmb/ultrarag
공식 코드UltraRAG: A Modular and Automated Toolkit for Adaptive Retrieval-Augmented Generation
https://github.com/openbmb/viscpm
공식 코드Large Multilingual Models Pivot Zero-Shot Multimodal Learning across Languages
https://github.com/openbmb/visrag
공식 코드VisRAG: Vision-based Retrieval-augmented Generation on Multi-modality Documents
https://github.com/opendatalab/OHR-Bench
공식 코드OCR Hinders RAG: Evaluating the Cascading Impact of OCR on Retrieval-Augmented Generation
https://github.com/opendatalab/OmniDocBench
공식 코드OmniDocBench: Benchmarking Diverse PDF Document Parsing with Comprehensive Annotations
https://github.com/opendatalab/openhueval
공식 코드OpenHuEval: Evaluating Large Language Model on Hungarian Specifics
https://github.com/opendfm/bai-scieval
공식 코드SciEval: A Multi-Level Large Language Model Evaluation Benchmark for Scientific Research
https://github.com/opendfm/rememberer
공식 코드Large Language Models Are Semi-Parametric Reinforcement Learning Agents
https://github.com/opendfm/scieval
공식 코드SciEval: A Multi-Level Large Language Model Evaluation Benchmark for Scientific Research
https://github.com/opengvlab/diffagent
공식 코드DiffAgent: Fast and Accurate Text-to-Image API Selection with Large Language Model
https://github.com/opengvlab/instruct2act
공식 코드Instruct2Act: Mapping Multi-modality Instructions to Robotic Actions with Large Language Model
https://github.com/opengvlab/interngpt
공식 코드VisionLLM: Large Language Model is also an Open-Ended Decoder for Vision-Centric Tasks
https://github.com/opengvlab/internvl
공식 코드InternVL: Scaling up Vision Foundation Models and Aligning for Generic Visual-Linguistic Tasks
https://github.com/opengvlab/omniquant
공식 코드OmniQuant: Omnidirectionally Calibrated Quantization for Large Language Models
https://github.com/opengvlab/piip
공식 코드Parameter-Inverted Image Pyramid Networks for Visual Perception and Multimodal Understanding
https://github.com/opengvlab/vinci
공식 코드An Egocentric Vision-Language Model based Portable Real-time Smart Assistant
https://github.com/opengvlab/visionllm
공식 코드VisionLLM: Large Language Model is also an Open-Ended Decoder for Vision-Centric Tasks
https://github.com/opening-up-chatgpt/opening-up-chatgpt.github.io
공식 코드https://github.com/openmatch/activerag
공식 코드ActiveRAG: Autonomously Knowledge Assimilation and Accommodation through Retrieval-Augmented Agents
https://github.com/openmatch/rag-ddr
공식 코드RAG-DDR: Optimizing Retrieval-Augmented Generation Using Differentiable Data Rewards
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.