Knowledge Graphs
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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/scunlp/bar
공식 코드BAR: A Backward Reasoning based Agent for Complex Minecraft Tasks
https://github.com/scut-dlvclab/tonggu-llm
공식 코드TongGu: Mastering Classical Chinese Understanding with Knowledge-Grounded Large Language Models
https://github.com/scxsunchenxi/test
공식 코드TEST: Text Prototype Aligned Embedding to Activate LLM's Ability for Time Series
https://github.com/sdamirsa/llm-vlm-in-gastroenterology
공식 코드https://github.com/sding11/RAG-VR
공식 코드RAG-VR: Leveraging Retrieval-Augmented Generation for 3D Question Answering in VR Environments
https://github.com/sdm-tib/eablock
공식 코드EABlock: A Declarative Entity Alignment Block for Knowledge Graph Creation Pipelines
https://github.com/se7esx/eib
공식 코드https://github.com/sebastian-steindl/CoPrUS_data
공식 코드CoPrUS: Consistency Preserving Utterance Synthesis towards more realistic benchmark dialogues
https://github.com/sebischair/kg-conv-exploratory-search
공식 코드https://github.com/sebischair/kg-in-nlp-survey
공식 코드A Decade of Knowledge Graphs in Natural Language Processing: A Survey
https://github.com/sebischair/llm-kg-d2t
공식 코드A Comparative Analysis of Conversational Large Language Models in Knowledge-Based Text Generation
https://github.com/sebischair/llm-sp-cqa
공식 코드https://github.com/sec-bench/sec-bench
공식 코드SEC-bench: Automated Benchmarking of LLM Agents on Real-World Software Security Tasks
https://github.com/seeyourmind/ptbox
공식 코드https://github.com/selbaez/evaluating-conversations-as-ekg
공식 코드Evaluating Agent Interactions Through Episodic Knowledge Graphs
https://github.com/selbeltagy/arragexperiments
공식 코드https://github.com/semantic-systems/elenglishwd
공식 코드https://github.com/semantic-systems/nfdi-search-engine-chatbot
공식 코드Scholarly Question Answering using Large Language Models in the NFDI4DataScience Gateway
https://github.com/semanto-mondal/A-RAG-Augmented-YOLOv8-Framework
공식 코드https://github.com/seohyunwoo-0407/gar
공식 코드https://github.com/sepermetric/seper
공식 코드SePer: Measure Retrieval Utility Through The Lens Of Semantic Perplexity Reduction
https://github.com/servicenow/agentada
공식 코드AgentAda: Skill-Adaptive Data Analytics for Tailored Insight Discovery
https://github.com/servicenow/browsergym
공식 코드WorkArena: How Capable Are Web Agents at Solving Common Knowledge Work Tasks?
https://github.com/servicenow/promptmix-emnlp-2023
공식 코드PromptMix: A Class Boundary Augmentation Method for Large Language Model Distillation
https://github.com/servicenow/workarena
공식 코드WorkArena: How Capable Are Web Agents at Solving Common Knowledge Work Tasks?
https://github.com/seu-kse/SparqlSession
공식 코드https://github.com/seukgcode/fastkge
공식 코드Fast and Continual Knowledge Graph Embedding via Incremental LoRA
https://github.com/seungheondoh/lp-music-caps
공식 코드https://github.com/seungheondoh/lp-music-dialog
공식 코드Music Discovery Dialogue Generation Using Human Intent Analysis and Large Language Models
https://github.com/seungheondoh/music-text-representation-pp
공식 코드Enriching Music Descriptions with a Finetuned-LLM and Metadata for Text-to-Music Retrieval
https://github.com/seungyoon1/llm-as-a-judge-human-eval
공식 코드Can Language Models Evaluate Human Written Text? Case Study on Korean Student Writing for Education
https://github.com/sevinjt/owl2vec4oa
공식 코드OWL2Vec4OA: Tailoring Knowledge Graph Embeddings for Ontology Alignment
https://github.com/seyedparsa/let-me-think
공식 코드Let Me Think! A Long Chain-of-Thought Can Be Worth Exponentially Many Short Ones
https://github.com/shafnehal/samcares
공식 코드https://github.com/shailaja183/objectconceptlearning
공식 코드Help Me Identify: Is an LLM+VQA System All We Need to Identify Visual Concepts?
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.