Papers with Code 아카이브
Large Language Model
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 2
- 평가표
- 0
- 보존된 평가 기록
- 0
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/wisdomshell/freeeval
공식 코드FreeEval: A Modular Framework for Trustworthy and Efficient Evaluation of Large Language Models
https://github.com/wishever/mmidr
공식 코드https://github.com/wisper12933/ga-rollback
공식 코드Generator-Assistant Stepwise Rollback Framework for Large Language Model Agent
https://github.com/wivizhang/earthgpt
공식 코드https://github.com/wivizhang/earthmarker
공식 코드EarthMarker: A Visual Prompting Multi-modal Large Language Model for Remote Sensing
https://github.com/wjj0122/arallm
공식 코드https://github.com/wlik/glrec
공식 코드Exploring Large Language Model for Graph Data Understanding in Online Job Recommendations
https://github.com/wlzhang2020/reasonrag
공식 코드Process vs. Outcome Reward: Which is Better for Agentic RAG Reinforcement Learning
https://github.com/wonderful-team-robotics/wonderful_team_robotics
공식 코드Wonderful Team: Zero-Shot Physical Task Planning with Visual LLMs
https://github.com/woooodyy/agentgym
공식 코드AgentGym: Evolving Large Language Model-based Agents across Diverse Environments
https://github.com/woooodyy/llm-agent-paper-list
공식 코드The Rise and Potential of Large Language Model Based Agents: A Survey
https://github.com/wpydcr/mes-rag
공식 코드MES-RAG: Bringing Multi-modal, Entity-Storage, and Secure Enhancements to RAG
https://github.com/writer/writing-in-the-margins
공식 코드Writing in the Margins: Better Inference Pattern for Long Context Retrieval
https://github.com/wuhy68/chatedav1
공식 코드ChatEDA: A Large Language Model Powered Autonomous Agent for EDA
https://github.com/wujiangxu/agenticmemory
공식 코드https://github.com/wujiangxu/slmrec
공식 코드SLMRec: Distilling Large Language Models into Small for Sequential Recommendation
https://github.com/wujwyi/pa-rag
공식 코드PA-RAG: RAG Alignment via Multi-Perspective Preference Optimization
https://github.com/wuklab/infercept
공식 코드InferCept: Efficient Intercept Support for Augmented Large Language Model Inference
https://github.com/wutong8023/versicode
공식 코드https://github.com/wuxingyu-ai/as-llm
공식 코드Large Language Model-Enhanced Algorithm Selection: Towards Comprehensive Algorithm Representation
https://github.com/wuxingyu-ai/llm4ec
공식 코드Evolutionary Computation in the Era of Large Language Model: Survey and Roadmap
https://github.com/wuyike2000/CoTKR
공식 코드CoTKR: Chain-of-Thought Enhanced Knowledge Rewriting for Complex Knowledge Graph Question Answering
https://github.com/wwwadx/FinVis-GPT
공식 코드FinVis-GPT: A Multimodal Large Language Model for Financial Chart Analysis
https://github.com/wyharturia/llm_enhanced_ml
공식 코드Large Language Model Enhanced Machine Learning Estimators for Classification
https://github.com/wyl-willing/MindMap
공식 코드MindMap: Knowledge Graph Prompting Sparks Graph of Thoughts in Large Language Models
https://github.com/wyripple/rp_kw_errors
공식 코드Revealing and Mitigating the Challenge of Detecting Character Knowledge Errors in LLM Role-Playing
https://github.com/wyu97/GenRead
공식 코드Generate rather than Retrieve: Large Language Models are Strong Context Generators
https://github.com/wyxstriker/MakeSomeNoiseInference
공식 코드Make Some Noise: Unlocking Language Model Parallel Inference Capability through Noisy Training
https://github.com/wzy232303/fpt
공식 코드FPT: Feature Prompt Tuning for Few-shot Readability Assessment
https://github.com/x-lance/neusym-rag
공식 코드NeuSym-RAG: Hybrid Neural Symbolic Retrieval with Multiview Structuring for PDF Question Answering
https://github.com/x-plug/mplug-docowl
공식 코드mPLUG-DocOwl: Modularized Multimodal Large Language Model for Document Understanding
https://github.com/x-plug/mplug-halowl
공식 코드Hallucination Augmented Contrastive Learning for Multimodal Large Language Model
https://github.com/x-plug/mplug-owl
공식 코드mPLUG-Owl2: Revolutionizing Multi-modal Large Language Model with Modality Collaboration
https://github.com/x-plug/multi-llm-agent
공식 코드https://github.com/x-plug/youku-mplug
공식 코드Youku-mPLUG: A 10 Million Large-scale Chinese Video-Language Dataset for Pre-training and Benchmarks
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.