https://github.com/wengsyx/lingyi
공식 코드LingYi: Medical Conversational Question Answering System based on Multi-modal Knowledge Graphs
Papers with Code 아카이브
Large Language Model
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코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/wenkehuang/awesome-mllm-tuning
공식 코드Keeping Yourself is Important in Downstream Tuning Multimodal Large Language Model
https://github.com/wenqian-zhang/kcd
공식 코드KCD: Knowledge Walks and Textual Cues Enhanced Political Perspective Detection in News Media
https://github.com/wentao-xu/kge-cl
공식 코드KGE-CL: Contrastive Learning of Tensor Decomposition Based Knowledge Graph Embeddings
https://github.com/wentaotan/mllm4text-reid
공식 코드Harnessing the Power of MLLMs for Transferable Text-to-Image Person ReID
https://github.com/weopenml/pandalm
공식 코드PandaLM: An Automatic Evaluation Benchmark for LLM Instruction Tuning Optimization
https://github.com/westlake-autolab/biokgbench.github.io
공식 코드BioKGBench: A Knowledge Graph Checking Benchmark of AI Agent for Biomedical Science
https://github.com/westlake-baichuan-mllm/bc-omni
공식 코드https://github.com/westlake-baichuan-mllm/ocean-omni
공식 코드https://github.com/wgcyeo/UniversalRAG
공식 코드UniversalRAG: Retrieval-Augmented Generation over Corpora of Diverse Modalities and Granularities
https://github.com/wghr123/mfgdiffusion
공식 코드MFGDiffusion: Mask-Guided Smoke Synthesis for Enhanced Forest Fire Detection
https://github.com/wglab/mint-llm
공식 코드https://github.com/wglab/reversal_curse
공식 코드https://github.com/whcpumpkin/demand-driven-navigation
공식 코드Find What You Want: Learning Demand-conditioned Object Attribute Space for Demand-driven Navigation
https://github.com/which47/llmcl
공식 코드Analyzing and Reducing Catastrophic Forgetting in Parameter Efficient Tuning
https://github.com/whu-zqh/kgan
공식 코드https://github.com/whuhsy/geo-fub
공식 코드https://github.com/wilkox/gptscreenr
공식 코드Automated title and abstract screening for scoping reviews using the GPT-4 Large Language Model
https://github.com/willemsenbram/mention-detection-vgd
공식 코드Detecting Referring Expressions in Visually Grounded Dialogue with Autoregressive Language Models
https://github.com/willemsenbram/reference-resolution-via-text-generation
공식 코드Resolving References in Visually-Grounded Dialogue via Text Generation
https://github.com/william-vw/elkg
공식 코드https://github.com/williambrach/gqr
공식 코드https://github.com/williamleif/graphqembed
공식 코드https://github.com/williamliujl/Qilin-Med
공식 코드Qilin-Med: Multi-stage Knowledge Injection Advanced Medical Large Language Model
https://github.com/williamzr/recipe2plan
공식 코드https://github.com/windylee0822/trea
공식 코드TREA: Tree-Structure Reasoning Schema for Conversational Recommendation
https://github.com/wisdommllab/herln
공식 코드https://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/wjk666/disenkgat
공식 코드DisenKGAT: Knowledge Graph Embedding with Disentangled Graph Attention Network
https://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
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.