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【第116期】LLM Inference-Time自我提升综述
Seventy3: 用NotebookLM将论文生成播客，让大家跟着AI一起进步。
今天的主题是：A Survey on LLM Inference-Time Self-ImprovementSummary
This research survey explores Large Language Model (LLM) Inference-Time Self-Improvement (ITSI), techniques enhancing LLM performance at inference without retraining. The authors categorize ITSI methods into three groups: Independent, improving decoding processes; Context-Aware, leveraging external context or data; and Model-Aided, using other models for collaboration. A comprehensive taxonomy of existing ITSI methods is presented, along with a discussion of challenges and future research directions, such as addressing biases and improving efficiency. The survey draws on recent publications from top AI conferences. Finally, ethical considerations, including bias and economic/environmental impact, are highlighted.
本研究综述探讨了大型语言模型（LLM）在推理阶段自我改进（Inference-Time Self-Improvement, ITSI）的技术，这些技术无需重新训练即可提升模型性能。作者将 ITSI 方法分为三类：独立型（优化解码过程）、上下文感知型（利用外部上下文或数据）和模型辅助型（借助其他模型协作）。文章提供了现有 ITSI 方法的全面分类，并讨论了当前的挑战和未来研究方向，如解决偏见问题和提高效率。该综述参考了最近顶级人工智能会议的研究成果。最后，文章还强调了伦理考量，包括偏见以及经济和环境影响。
原文链接：https://arxiv.org/abs/2412.14352
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By 任雨山Seventy3: 用NotebookLM将论文生成播客，让大家跟着AI一起进步。
今天的主题是：A Survey on LLM Inference-Time Self-ImprovementSummary
This research survey explores Large Language Model (LLM) Inference-Time Self-Improvement (ITSI), techniques enhancing LLM performance at inference without retraining. The authors categorize ITSI methods into three groups: Independent, improving decoding processes; Context-Aware, leveraging external context or data; and Model-Aided, using other models for collaboration. A comprehensive taxonomy of existing ITSI methods is presented, along with a discussion of challenges and future research directions, such as addressing biases and improving efficiency. The survey draws on recent publications from top AI conferences. Finally, ethical considerations, including bias and economic/environmental impact, are highlighted.
本研究综述探讨了大型语言模型（LLM）在推理阶段自我改进（Inference-Time Self-Improvement, ITSI）的技术，这些技术无需重新训练即可提升模型性能。作者将 ITSI 方法分为三类：独立型（优化解码过程）、上下文感知型（利用外部上下文或数据）和模型辅助型（借助其他模型协作）。文章提供了现有 ITSI 方法的全面分类，并讨论了当前的挑战和未来研究方向，如解决偏见问题和提高效率。该综述参考了最近顶级人工智能会议的研究成果。最后，文章还强调了伦理考量，包括偏见以及经济和环境影响。
原文链接：https://arxiv.org/abs/2412.14352