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KDD 2025Workshop Summaries

KDD 2025 Workshop on Inference Optimization for Generative AI

Panpan Xu, Youngsuk Park, Lin Lee Cheong, Yida Wang 0003, Yiying Zhang 0005, George Karypis, Sherry Marcus

PDF 由论文原始站点提供,PaperCompass 不保存论文文件。DOI 10.1145/3711896.3737865 ↗

摘要

The demand for efficient Large Language Model (LLM) inference has surged with the rising adoption of Generative AI (GenAI) applications, particularly in areas such as agents and retrieval-augmented generation. Efficient inference serves two crucial purposes: it enables the deployment of LLM-centered applications that address critical business needs, while also facilitating rapid experimentation for researchers to extract valuable insights and new understandings. However, despite the field's rapid advancement and interdisciplinary nature, there remains a limited exchange of ideas and methodologies between production-facing practitioners and researchers seeking to experiment with new GenAI concepts quickly. To bridge this gap, we are introducing the first KDD workshop on Inference Optimization for Generative AI. Our goal is to create a collaborative platform where researchers and practitioners working across various use cases and stacks of efficient inference can come together to exchange research ideas, establish connections between different disciplines, and identify challenges and research questions that will shape future work.