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/mrzilinxiao/autover
공식 코드https://github.com/msorkhpar/wiki-entity-summarization
공식 코드https://github.com/mstubbemann/Orometric-Methods-in-Bounded-Metric-Data
공식 코드https://github.com/mstubbemann/lg4av
공식 코드LG4AV: Combining Language Models and Graph Neural Networks for Author Verification
https://github.com/mtkresearch/rad-bench
공식 코드RAD-Bench: Evaluating Large Language Models Capabilities in Retrieval Augmented Dialogues
https://github.com/mu-data-science/gaf
공식 코드A Scalable Tool For Analyzing Genomic Variants Of Humans Using Knowledge Graphs and Machine Learning
https://github.com/muellerlab/synthesizing_interpretable_control_policies
공식 코드Synthesizing Interpretable Control Policies through Large Language Model Guided Search
https://github.com/muguruzawang/kgsum
공식 코드Multi-Document Scientific Summarization from a Knowledge Graph-Centric View
https://github.com/muktac5/ciir-liverag
공식 코드CIIR@LiveRAG 2025: Optimizing Multi-Agent Retrieval Augmented Generation through Self-Training
https://github.com/multimodal-art-projection/map-neo
공식 코드MAP-Neo: Highly Capable and Transparent Bilingual Large Language Model Series
https://github.com/mungomeng/survival-xsurv
공식 코드Merging-Diverging Hybrid Transformer Networks for Survival Prediction in Head and Neck Cancer
https://github.com/murongyue/llm_mot_cascade
공식 코드Large Language Model Cascades with Mixture of Thoughts Representations for Cost-efficient Reasoning
https://github.com/mushfiqur11/simpledc-dataset
공식 코드https://github.com/mustafakarabag/llmchameleon
공식 코드https://github.com/mutiann/ccc
공식 코드On the Role of Conceptualization in Commonsense Knowledge Graph Construction
https://github.com/mxpoliakov/multi-meta-rag
공식 코드https://github.com/my-ai-university/finite-element-method
공식 코드AI-University: An LLM-based platform for instructional alignment to scientific classrooms
https://github.com/myazann/ap-bots
공식 코드Improving RAG for Personalization with Author Features and Contrastive Examples
https://github.com/myeon9h/planrag
공식 코드https://github.com/mysong7nlper/chatgpt_as_keyphrase_extractor
공식 코드Large Language Models as Zero-Shot Keyphrase Extractors: A Preliminary Empirical Study
https://github.com/n28div/llm-disjointness
공식 코드Enriching Ontologies with Disjointness Axioms using Large Language Models
https://github.com/n4feng/ResponseQualityAssessment
공식 코드Response Quality Assessment for Retrieval-Augmented Generation via Conditional Conformal Factuality
https://github.com/nadavlab/CPLLM
공식 코드https://github.com/naist-nlp/CC_RAG
공식 코드https://github.com/namin/llm-verified-with-monte-carlo-tree-search
공식 코드VerMCTS: Synthesizing Multi-Step Programs using a Verifier, a Large Language Model, and Tree Search
https://github.com/nan-huo/micro-act
공식 코드Micro-Act: Mitigate Knowledge Conflict in Question Answering via Actionable Self-Reasoning
https://github.com/narabzad/genir-evaluation
공식 코드https://github.com/narabzad/prompt-sensitivity-relevance-judgements
공식 코드A Human-AI Comparative Analysis of Prompt Sensitivity in LLM-Based Relevance Judgment
https://github.com/nasrinkalanat/skg-sym
공식 코드https://github.com/national-center-for-ai-saudi-arabia/lm-evaluation-harness
공식 코드When Benchmarks are Targets: Revealing the Sensitivity of Large Language Model Leaderboards
https://github.com/naumannaeem/bea_2025
공식 코드https://github.com/naver-ai/korean-safety-benchmarks
공식 코드KoSBi: A Dataset for Mitigating Social Bias Risks Towards Safer Large Language Model Application
https://github.com/naver-ai/tablevqabench
공식 코드TableVQA-Bench: A Visual Question Answering Benchmark on Multiple Table Domains
https://github.com/naver-intel-co-lab/gaudi-lavcap
공식 코드LAVCap: LLM-based Audio-Visual Captioning using Optimal Transport
https://github.com/naver/bergen
공식 코드BERGEN: A Benchmarking Library for Retrieval-Augmented Generation
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.