Papers with Code 아카이브

Knowledge Graph Completion

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

전체 기법 전체 데이터셋

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: $(h, r, ?)$.

Source: One-Shot Relational Learning for Knowledge Graphs

코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/yisuanwang/soulstyler

공식 코드

Soulstyler: Using Large Language Model to Guide Image Style Transfer for Target Object

https://github.com/yiveen/syntheticdatafeasibility

공식 코드

Does Feasibility Matter? Understanding the Impact of Feasibility on Synthetic Training Data

https://github.com/yixiangch/mrd-rag

공식 코드

MRD-RAG: Enhancing Medical Diagnosis with Multi-Round Retrieval-Augmented Generation

https://github.com/yixuantt/MultiHop-RAG

공식 코드

MultiHop-RAG: Benchmarking Retrieval-Augmented Generation for Multi-Hop Queries

https://github.com/yizhilll/transher

공식 코드

TranSHER: Translating Knowledge Graph Embedding with Hyper-Ellipsoidal Restriction

https://github.com/yjeugenia/maferw

공식 코드

MaFeRw: Query Rewriting with Multi-Aspect Feedbacks for Retrieval-Augmented Large Language Models

https://github.com/yjiangcm/lion

공식 코드

Lion: Adversarial Distillation of Proprietary Large Language Models

https://github.com/yjsoh/youra

공식 코드

You Only Use Reactive Attention Slice For Long Context Retrieval

https://github.com/ykumards/eclip

공식 코드

Improving Medical Multi-modal Contrastive Learning with Expert Annotations

https://github.com/ym689/rec_icl

공식 코드

Real-Time Personalization for LLM-based Recommendation with Customized In-Context Learning

https://github.com/ymoslem/adaptive-mt-llm-fine-tuning

공식 코드

Fine-tuning Large Language Models for Adaptive Machine Translation

https://github.com/yoadtew/zero-shot-image-to-text

공식 코드

ZeroCap: Zero-Shot Image-to-Text Generation for Visual-Semantic Arithmetic

https://github.com/yoda-lab/dialectical-reconciliation-with-structured-argumentation

공식 코드

Dialectical Reconciliation via Structured Argumentative Dialogues

https://github.com/yoda-lab/persona

공식 코드

Does Your AI Agent Get You? A Personalizable Framework for Approximating Human Models from Argumentation-based Dialogue Traces

https://github.com/yongaifadian1/Dynamic-Parameter-Memory_DPM

공식 코드

Dynamic Parameter Memory: Temporary LoRA-Enhanced LLM for Long-Sequence Emotion Recognition in Conversation

https://github.com/yongchao98/promst

공식 코드

PRompt Optimization in Multi-Step Tasks (PROMST): Integrating Human Feedback and Heuristic-based Sampling

https://github.com/yosukehiguchi/espnet

공식 코드

Harnessing the Zero-Shot Power of Instruction-Tuned Large Language Model in End-to-End Speech Recognition

https://github.com/you68681/lpw

공식 코드

Planning-Driven Programming: A Large Language Model Programming Workflow

https://github.com/youngdubbydu/llm-agent-optimization

공식 코드

A Survey on the Optimization of Large Language Model-based Agents

https://github.com/youngerous/qtree

공식 코드

Learning to Explore and Select for Coverage-Conditioned Retrieval-Augmented Generation

https://github.com/youngseungjeon/ragppi

공식 코드

RAGPPI: RAG Benchmark for Protein-Protein Interactions in Drug Discovery

https://github.com/ys-feng/DemoGraph

공식 코드

Democratizing Large Language Model-Based Graph Data Augmentation via Latent Knowledge Graphs

https://github.com/ysh-1998/csrec

공식 코드

Common Sense Enhanced Knowledge-based Recommendation with Large Language Model

https://github.com/ysh-1998/lrd

공식 코드

Sequential Recommendation with Latent Relations based on Large Language Model

https://github.com/ytomc/autoassumpdl

공식 코드

An Exploratory Study on Automatic Identification of Assumptions in the Development of Deep Learning Frameworks

https://github.com/ytongxie/pairaug

공식 코드

PairAug: What Can Augmented Image-Text Pairs Do for Radiology?

https://github.com/ytyz1307zzh/minimally_edited_questions

공식 코드

Exploring Contrast Consistency of Open-Domain Question Answering Systems on Minimally Edited Questions

https://github.com/yu-rp/dimple

공식 코드

Dimple: Discrete Diffusion Multimodal Large Language Model with Parallel Decoding

https://github.com/yu-rp/visualperceptiontoken

공식 코드

Introducing Visual Perception Token into Multimodal Large Language Model

https://github.com/yuan-li-fnlp/r3-rag

공식 코드

R3-RAG: Learning Step-by-Step Reasoning and Retrieval for LLMs via Reinforcement Learning

https://github.com/yuanbo2020/soundscaper

공식 코드

Soundscape Captioning using Sound Affective Quality Network and Large Language Model

https://github.com/yuanninghuang/thesis

공식 코드

Unveiling Gender Bias in Large Language Models: Using Teacher's Evaluation in Higher Education As an Example

https://github.com/yuanyehome/palt

공식 코드

PALT: Parameter-Lite Transfer of Language Models for Knowledge Graph Completion

https://github.com/yuchen2199/explainable-driver-attention-prediction

공식 코드

Where, What, Why: Towards Explainable Driver Attention Prediction

https://github.com/yuchengz99/trix

공식 코드

TRIX: A More Expressive Model for Zero-shot Domain Transfer in Knowledge Graphs

https://github.com/yuchenxia/aasbyllm

공식 코드

Generation of Asset Administration Shell with Large Language Model Agents: Toward Semantic Interoperability in Digital Twins in the Context of Industry 4.0

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.