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Large Language Model

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

https://github.com/Nouf-Alabbasi/oKUmura_AI_Telecom_challenge

공식 코드

Leveraging Fine-Tuned Retrieval-Augmented Generation with Long-Context Support: For 3GPP Standards

https://github.com/OPPOMKLab/u-LLaVA

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u-LLaVA: Unifying Multi-Modal Tasks via Large Language Model

https://github.com/OPTML-Group/Unlearn-Simple

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Simplicity Prevails: Rethinking Negative Preference Optimization for LLM Unlearning

https://github.com/OceannTwT/Tool-Planner

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Tool-Planner: Task Planning with Clusters across Multiple Tools

https://github.com/OmarErak/LLM-CL

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Large Language Model-Driven Curriculum Design for Mobile Networks

https://github.com/OpenBMB/AgentCPM-GUI

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AgentCPM-GUI: Building Mobile-Use Agents with Reinforcement Fine-Tuning

https://github.com/OpenDFM/MobA

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MobA: Multifaceted Memory-Enhanced Adaptive Planning for Efficient Mobile Task Automation

https://github.com/OpenSparseLLMs/CLIP-MoE

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CLIP-MoE: Towards Building Mixture of Experts for CLIP with Diversified Multiplet Upcycling

https://github.com/OpenStellarTeam/ChineseEcomQA

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ChineseEcomQA: A Scalable E-commerce Concept Evaluation Benchmark for Large Language Models

https://github.com/PJLab-ADG/driveLikeAHuman

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Drive Like a Human: Rethinking Autonomous Driving with Large Language Models

https://github.com/PKU-YuanGroup/LLMBind

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LLMBind: A Unified Modality-Task Integration Framework

https://github.com/PKU-YuanGroup/Video-LLaVA

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Video-LLaVA: Learning United Visual Representation by Alignment Before Projection

https://github.com/PKUDigitalHealth/KidneyTalk-open

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KidneyTalk-open: No-code Deployment of a Private Large Language Model with Medical Documentation-Enhanced Knowledge Database for Kidney Disease

https://github.com/PLUM-Lab/AR-RAG

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AR-RAG: Autoregressive Retrieval Augmentation for Image Generation

https://github.com/PaddlePaddle/PaddleMIX

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PP-DocBee: Improving Multimodal Document Understanding Through a Bag of Tricks

https://github.com/Parry-Parry/terrier_trec_2023

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Generative Relevance Feedback and Convergence of Adaptive Re-Ranking: University of Glasgow Terrier Team at TREC DL 2023

https://github.com/PasiniSamuele/Robust-Attack-Detectors-LLM

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Evaluating and Improving the Robustness of Security Attack Detectors Generated by LLMs

https://github.com/Pie115/Learning-Matrix-Functions-In-Context

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Can a Large Language Model Learn Matrix Functions In Context?

https://github.com/Pints-AI/Finetune-Bench-RAG

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Finetune-RAG: Fine-Tuning Language Models to Resist Hallucination in Retrieval-Augmented Generation

https://github.com/Pro-GenAI/AutoPureData

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AutoPureData: Automated Filtering of Undesirable Web Data to Update LLM Knowledge

https://github.com/PyRGL/rgl

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RGL: A Graph-Centric, Modular Framework for Efficient Retrieval-Augmented Generation on Graphs

https://github.com/QwenLM/Qwen-7B

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Qwen Technical Report

https://github.com/RED-Search/Qilin

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Qilin: A Multimodal Information Retrieval Dataset with APP-level User Sessions

https://github.com/RLE-Foundation/rllte

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RLLTE: Long-Term Evolution Project of Reinforcement Learning

https://github.com/RLHFlow/RLHF-Reward-Modeling

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RLHF Workflow: From Reward Modeling to Online RLHF

https://github.com/RManLuo/gfm-rag

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GFM-RAG: Graph Foundation Model for Retrieval Augmented Generation

https://github.com/RUC-GSAI/YuLan-OneSim

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YuLan-OneSim: Towards the Next Generation of Social Simulator with Large Language Models

https://github.com/RadeenXALNW/G-RAG_1.0

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G-RAG: Knowledge Expansion in Material Science

https://github.com/Rain9876/ShareLoRA

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ShareLoRA: Parameter Efficient and Robust Large Language Model Fine-tuning via Shared Low-Rank Adaptation

https://github.com/RebelsNLU-jaist/llm-unlearning

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On Effects of Steering Latent Representation for Large Language Model Unlearning

https://github.com/RefalMachine/llmtf_open

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Facilitating large language model Russian adaptation with Learned Embedding Propagation

https://github.com/RezzFayyazi/TTP-LLM

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Advancing TTP Analysis: Harnessing the Power of Large Language Models with Retrieval Augmented Generation

https://github.com/RotsteinNoam/FuseCap

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FuseCap: Leveraging Large Language Models for Enriched Fused Image Captions

https://github.com/RotsteinNoam/Paint-by-Inpaint

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Paint by Inpaint: Learning to Add Image Objects by Removing Them First

https://github.com/RujingYao/LSIM

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Elevating Legal LLM Responses: Harnessing Trainable Logical Structures and Semantic Knowledge with Legal Reasoning

https://github.com/RyotaroOKabe/llm4syn

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Large Language Model-Guided Prediction Toward Quantum Materials Synthesis

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