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/kimho666/llm_hardware_survey
공식 코드Large Language Model Inference Acceleration: A Comprehensive Hardware Perspective
https://github.com/kinianlo/contextuality-in-llm
공식 코드https://github.com/kitft/funsearch
공식 코드https://github.com/kkskp/re-rag
공식 코드https://github.com/kkteru/grail
공식 코드https://github.com/kkyuhun94/dalda
공식 코드DALDA: Data Augmentation Leveraging Diffusion Model and LLM with Adaptive Guidance Scaling
https://github.com/klarna/product-page-dataset
공식 코드https://github.com/kmatton/walk-the-talk
공식 코드Walk the Talk? Measuring the Faithfulness of Large Language Model Explanations
https://github.com/kmswin1/comdense
공식 코드https://github.com/kmswin1/qpaug
공식 코드QPaug: Question and Passage Augmentation for Open-Domain Question Answering of LLMs
https://github.com/knowledge-ukraine/ontochatgpt
공식 코드OntoChatGPT Information System: Ontology-Driven Structured Prompts for ChatGPT Meta-Learning
https://github.com/komeijiforce/emojilm
공식 코드https://github.com/kortukov/realistic_knowledge_conflicts
공식 코드Studying Large Language Model Behaviors Under Context-Memory Conflicts With Real Documents
https://github.com/krafton-ai/rare-to-frequent
공식 코드https://github.com/krandiash/octopus
공식 코드Octopus: A Framework for Cost-Quality-Time Optimization in Crowdsourcing
https://github.com/krishgoel/chronocept-baseline-models
공식 코드https://github.com/krishgoel/hiro
공식 코드https://github.com/krr-oxford/oet
공식 코드Ontology Enrichment from Texts: A Biomedical Dataset for Concept Discovery and Placement
https://github.com/krystalan/RAGtrans
공식 코드Retrieval-Augmented Machine Translation with Unstructured Knowledge
https://github.com/krzentner/language-world
공식 코드Conditionally Combining Robot Skills using Large Language Models
https://github.com/ktlktl/c-llm
공식 코드C-LLM: Learn to Check Chinese Spelling Errors Character by Character
https://github.com/kuanchihhuang/reason3d
공식 코드Reason3D: Searching and Reasoning 3D Segmentation via Large Language Model
https://github.com/kuehnram/findyourfigure
공식 코드Enhancing Rhetorical Figure Annotation: An Ontology-Based Web Application with RAG Integration
https://github.com/kumarmanas/SHIFT
공식 코드Uncertainty-Aware Trajectory Prediction via Rule-Regularized Heteroscedastic Deep Classification
https://github.com/kushagrabhushan/Systematic-Knowledge-Injection
공식 코드https://github.com/kutaytire/retrieval-augmented-time-series-forecasting
공식 코드https://github.com/kyle8581/Web-Shepherd
공식 코드https://github.com/kylrth/procedure-generation
공식 코드Pairing Analogy-Augmented Generation with Procedural Memory for Procedural Q&A
https://github.com/kyutai-labs/moshi
공식 코드Moshi: a speech-text foundation model for real-time dialogue
https://github.com/l8cv/busterx
공식 코드BusterX: MLLM-Powered AI-Generated Video Forgery Detection and Explanation
https://github.com/lab-v2/chatgpt_mwp_eval
공식 코드An Independent Evaluation of ChatGPT on Mathematical Word Problems (MWP)
https://github.com/lab-v2/diversity_measures
공식 코드Diversity Measures: Domain-Independent Proxies for Failure in Language Model Queries
https://github.com/laihaoran/carzero
공식 코드CARZero: Cross-Attention Alignment for Radiology Zero-Shot Classification
https://github.com/laiyuzhi/nvp-hri
공식 코드NVP-HRI: Zero Shot Natural Voice and Posture-based Human-Robot Interaction via Large Language Model
https://github.com/lakeraai/dsec-gandalf
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.