Papers with Code 아카이브
Knowledge Graph Completion
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- 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/jianhaochen-nju/tsdre
공식 코드Timeline-based Sentence Decomposition with In-Context Learning for Temporal Fact Extraction
https://github.com/jianlingl/llm_st_constparsing
공식 코드LLM-enhanced Self-training for Cross-domain Constituency Parsing
https://github.com/jianshuod/engorgio-prompt
공식 코드https://github.com/jianxu95/rallrec
공식 코드https://github.com/jianyu-cs/promptquine
공식 코드Evolving Prompts In-Context: An Open-ended, Self-replicating Perspective
https://github.com/jiapuwang/awesome-tkgc
공식 코드A Survey on Temporal Knowledge Graph Completion: Taxonomy, Progress, and Prospects
https://github.com/jiaruzouu/prb
공식 코드https://github.com/jieyilong/tree-of-thought-puzzle-solver
공식 코드https://github.com/jihaonew/mm-instruct
공식 코드MM-Instruct: Generated Visual Instructions for Large Multimodal Model Alignment
https://github.com/jiho283/FactKG
공식 코드https://github.com/jiho283/kg-gpt
공식 코드KG-GPT: A General Framework for Reasoning on Knowledge Graphs Using Large Language Models
https://github.com/jimengshi/hypercube-rag
공식 코드https://github.com/jimmyma99/teleantifraud
공식 코드TeleAntiFraud-28k: An Audio-Text Slow-Thinking Dataset for Telecom Fraud Detection
https://github.com/jindongli-ai/next-generation-llm-based-recommender-systems-survey
공식 코드Towards Next-Generation LLM-based Recommender Systems: A Survey and Beyond
https://github.com/jinggqu/nextgen-uia
공식 코드Adapting Vision-Language Foundation Model for Next Generation Medical Ultrasound Image Analysis
https://github.com/jinghanzhang1998/ratt
공식 코드RATT: A Thought Structure for Coherent and Correct LLM Reasoning
https://github.com/jingkang50/openpvsg
공식 코드https://github.com/jingkang50/psg4d
공식 코드https://github.com/jinlab-imvr/medagent-pro
공식 코드MedAgent-Pro: Towards Evidence-based Multi-modal Medical Diagnosis via Reasoning Agentic Workflow
https://github.com/jiny1623/dynamicer
공식 코드DynamicER: Resolving Emerging Mentions to Dynamic Entities for RAG
https://github.com/jinyangwu/NoiserBench
공식 코드https://github.com/jinyansu1/aggd
공식 코드https://github.com/jinyansu1/eval_poisonrag
공식 코드https://github.com/jinyansu1/flare-aug
공식 코드https://github.com/jinzhuoran/rag-rewardbench
공식 코드https://github.com/jiyaowei/lp_nkgs
공식 코드https://github.com/jizhi-zhang/billp
공식 코드Large Language Models are Learnable Planners for Long-Term Recommendation
https://github.com/jizhi-zhang/fairllm
공식 코드Is ChatGPT Fair for Recommendation? Evaluating Fairness in Large Language Model Recommendation
https://github.com/jizhi-zhang/rec4agentverse_case
공식 코드Prospect Personalized Recommendation on Large Language Model-based Agent Platform
https://github.com/jjcherian/conformal-safety
공식 코드Large language model validity via enhanced conformal prediction methods
https://github.com/jjzha/fs1
공식 코드Scaling Reasoning can Improve Factuality in Large Language Models
https://github.com/jjzha/sefl
공식 코드SEFL: Harnessing Large Language Model Agents to Improve Educational Feedback Systems
https://github.com/jl-wei/feature-inspiration
공식 코드Getting Inspiration for Feature Elicitation: App Store- vs. LLM-based Approach
https://github.com/jlab-nlp/retrieval-augmented-correction
공식 코드RAC: Efficient LLM Factuality Correction with Retrieval Augmentation
https://github.com/jlcmoore/llms-as-therapists
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.