https://github.com/webis-de/scolia25-xsum
공식 코드Ask, Retrieve, Summarize: A Modular Pipeline for Scientific Literature Summarization
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/webis-de/sigir25-rag-crowdsourcing
공식 코드https://github.com/wecoai/aideml
공식 코드RE-Bench: Evaluating frontier AI R&D capabilities of language model agents against human experts
https://github.com/weichenzh/open3dvqa
공식 코드https://github.com/weihaox/UMBRAE
공식 코드https://github.com/weihuanglin/inf-llava
공식 코드INF-LLaVA: Dual-perspective Perception for High-Resolution Multimodal Large Language Model
https://github.com/weiiguo/wireless-agent
공식 코드WirelessAgent: Large Language Model Agents for Intelligent Wireless Networks
https://github.com/weijiawu/paradiffusion
공식 코드Paragraph-to-Image Generation with Information-Enriched Diffusion Model
https://github.com/weikang-wang/NationalityBiasOfChatGPT
공식 코드Quite Good, but Not Enough: Nationality Bias in Large Language Models -- A Case Study of ChatGPT
https://github.com/weiminxiong/ipr
공식 코드Watch Every Step! LLM Agent Learning via Iterative Step-Level Process Refinement
https://github.com/weimo3221/FUZZ-CODER
공식 코드https://github.com/weitaikang/robin3d
공식 코드Robin3D: Improving 3D Large Language Model via Robust Instruction Tuning
https://github.com/weiyanbin1999/kicgpt
공식 코드KICGPT: Large Language Model with Knowledge in Context for Knowledge Graph Completion
https://github.com/weiyifan1023/senator
공식 코드Structural Entropy Guided Agent for Detecting and Repairing Knowledge Deficiencies in LLMs
https://github.com/weiyumou/KCluster
공식 코드KCluster: An LLM-based Clustering Approach to Knowledge Component Discovery
https://github.com/weizhepei/instructrag
공식 코드InstructRAG: Instructing Retrieval-Augmented Generation via Self-Synthesized Rationales
https://github.com/wellecks/llmstep
공식 코드https://github.com/wenboown/generative-ai-for-semiconductor-physical-design
공식 코드Enhancing Reasoning to Adapt Large Language Models for Domain-Specific Applications
https://github.com/wendy-xiao/chatgpt_editing_summ
공식 코드Personalized Abstractive Summarization by Tri-agent Generation Pipeline
https://github.com/wenkehuang/awesome-mllm-tuning
공식 코드Keeping Yourself is Important in Downstream Tuning Multimodal Large Language Model
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/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/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/williambrach/gqr
공식 코드https://github.com/williamliujl/Qilin-Med
공식 코드Qilin-Med: Multi-stage Knowledge Injection Advanced Medical Large Language Model
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.