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yinizhilian/ICLR2025-Papers-with-Code

⭐ 588 added to this list on 2025-04-09 repository created 2024-05-14

This project is a comprehensive collection of papers and open-source projects from the International Conference on Learning Representations (ICLR) spanning multiple years including 2021, 2022, 2023, 2024, and 2025. It focuses particularly on the latest research advancements in large language models (LLMs) and various directions within the natural language processing (NLP) field. The repository is regularly updated to include new research findings and code implementations, making it a valuable resource for researchers, practitioners, and enthusiasts who want to stay current with cutting-edge developments in AI and machine learning. The repository provides curated links to papers and their corresponding codebases, facilitating easy access to state-of-the-art research and practical implementations. It includes a wide range of topics such as knowledge editing in LLMs, robustness in representation learning, graph generation models, multilingual language understanding, vision-language models, and more. Each entry typically contains a direct link to the paper and the associated code repository or dataset, enabling users to explore and reproduce the research results. Additionally, the project promotes community engagement through invitations to watch, fork, and star the repository. It also connects users to related resources such as a Zhihu profile and a WeChat public account that provide daily updates on AI research. The repository is part of a broader ecosystem that includes a dedicated community platform offering specialized columns on large model agents, inference, retrieval-augmented generation (RAG) systems, datasets, benchmarks, industry trends, and job referrals. Overall, this project serves as a centralized hub for accessing and tracking the latest ICLR papers and their code, supporting the AI research community in advancing knowledge and fostering collaboration.

https://github.com/yinizhilian/ICLR2025-Papers-with-Code

aibenchmarkscodecommunitydatasetsdeep-learning-papergemminigptgraph-generationiclriclr2021iclr2022iclr2023iclr2024industry-trendsknowledge-editinglarge-language-modelsllama3llmllm-agentllm-frameworkllm-reasoningllm-trainingllmsmachine-learningmultilingual-understandingnatural-language-processingnlpnlp-keywords-extractionnlp-machine-learningopen-source-projectspaperspaperwithcodepythonragresearchretrieval-augmented-generationrobustnesstransformerupdatesvision-language-models