Papers with Code 아카이브

Large Language Model

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

https://github.com/yang3121099/LLM-Neo

LLM-Neo: Parameter Efficient Knowledge Distillation for Large Language Models

https://github.com/yangletliu/finlora

BloombergGPT: A Large Language Model for Finance

https://github.com/yangyijune/mma-diffusion

GuardT2I: Defending Text-to-Image Models from Adversarial Prompts

https://github.com/yangyucheng000/Papers/tree/main/MindSpore-Retrieval-augmented

Modeling Relational Data with Graph Convolutional Networks

https://github.com/yangyucheng000/University/tree/main/model-1/rag

RAGA: Relation-aware Graph Attention Networks for Global Entity Alignment

https://github.com/yangyucheng000/University/tree/main/model-1/realm

REALM: RAG-Driven Enhancement of Multimodal Electronic Health Records Analysis via Large Language Models

https://github.com/yangyucheng000/University/tree/main/model-3/rag

Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks

https://github.com/yangyucheng000/University/tree/main/model-3/realm

REALM: RAG-Driven Enhancement of Multimodal Electronic Health Records Analysis via Large Language Models

https://github.com/yangyucheng000/VOGUE

VOGUE: Answer Verbalization through Multi-Task Learning

https://github.com/yangyucheng000/multi-basis-encoding

Inductive Knowledge Graph Reasoning for Multi-batch Emerging Entities

https://github.com/yangzhengyi98/dreamrec

Large Language Model Can Interpret Latent Space of Sequential Recommender

https://github.com/yaof20/real

HybridFlow: A Flexible and Efficient RLHF Framework

https://github.com/yeounoh/lc_nl2sql

CHESS: Contextual Harnessing for Efficient SQL Synthesis

https://github.com/yhzhu99/medagentboard

ColaCare: Enhancing Electronic Health Record Modeling through Large Language Model-Driven Multi-Agent Collaboration

https://github.com/ymoslem/opennmt-tutorial

Fine-tuning Large Language Models for Adaptive Machine Translation

https://github.com/youngsheen/gpst

Talk With Human-like Agents: Empathetic Dialogue Through Perceptible Acoustic Reception and Reaction

https://github.com/ytbai/ogbn_arxiv_dgl

Open Graph Benchmark: Datasets for Machine Learning on Graphs

https://github.com/yu-yang-li/starglm

Deep Learning and LLM-based Methods Applied to Stellar Lightcurve Classification

https://github.com/yuleiniu/rva

Visual Dialog

https://github.com/yuxiaw/openfactcheck

RARR: Researching and Revising What Language Models Say, Using Language Models

https://github.com/yuyuz/Variational-Reasoning-Networks

Variational Reasoning for Question Answering with Knowledge Graph

https://github.com/z-qiyu/KGNN-LS

Knowledge-aware Graph Neural Networks with Label Smoothness Regularization for Recommender Systems

https://github.com/zebangcheng/emotion-llama

MiniGPT-v2: large language model as a unified interface for vision-language multi-task learning

https://github.com/zehong-wang/gft

A Foundation Model for Zero-shot Logical Query Reasoning

https://github.com/zetangforward/lcm_stack

L-CiteEval: Do Long-Context Models Truly Leverage Context for Responding?

https://github.com/zhaoolee/garss

AgentOps: Enabling Observability of LLM Agents

https://github.com/zhengli97/promptkd

ATPrompt: Textual Prompt Learning with Embedded Attributes

https://github.com/zhengxujosh/awesome-rag-vision

Retrieval Augmented Generation and Understanding in Vision: A Survey and New Outlook

https://github.com/zhenqincn/fedkseed

Federated Full-Parameter Tuning of Billion-Sized Language Models with Communication Cost under 18 Kilobytes

https://github.com/zhongpeixiang/ckc

Keyword-Guided Neural Conversational Model

https://github.com/zhuang-li/factual

Scene Graph Parsing as Dependency Parsing

https://github.com/zihanzhaosjtu/librisqa

PMC-VQA: Visual Instruction Tuning for Medical Visual Question Answering

https://github.com/ziyuguo99/image-generation-cot

MAVIS: Mathematical Visual Instruction Tuning with an Automatic Data Engine

https://github.com/zjs123/streame

Diachronic Embedding for Temporal Knowledge Graph Completion

https://github.com/zju-dbl/metakg

KGAT: Knowledge Graph Attention Network for Recommendation

https://github.com/zjukg/snag

Knowledge Graphs Meet Multi-Modal Learning: A Comprehensive Survey

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