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【第102期】Byte Latent Transformer (BLT)：用byte级替代token级
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：Byte Latent Transformer: Patches Scale Better Than TokensSummary
The paper introduces the Byte Latent Transformer (BLT), a novel large language model architecture that processes raw byte data without tokenization. BLT dynamically groups bytes into patches based on entropy, allocating computational resources efficiently. Experimental results demonstrate BLT's competitive performance with tokenization-based models, particularly showcasing improved inference efficiency and robustness to noisy input. The research includes a comprehensive scaling study and ablation analysis, highlighting the advantages of BLT's patch-based approach over traditional tokenization. The authors release the code for BLT to facilitate further research.
本文介绍了字节潜变换器（Byte Latent Transformer，BLT），一种新型的大型语言模型架构，该架构直接处理原始字节数据，无需进行分词。BLT 基于熵动态地将字节分组为补丁，从而高效分配计算资源。实验结果表明，BLT 在推理效率和对噪声输入的鲁棒性方面表现出色，其性能可与基于分词的模型相媲美。研究还进行了全面的规模化研究和消融分析，突出了 BLT 的基于补丁方法相较传统分词方法的优势。作者发布了 BLT 的代码，以促进进一步研究。
原文链接：https://arxiv.org/abs/2412.09871
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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：Byte Latent Transformer: Patches Scale Better Than TokensSummary
The paper introduces the Byte Latent Transformer (BLT), a novel large language model architecture that processes raw byte data without tokenization. BLT dynamically groups bytes into patches based on entropy, allocating computational resources efficiently. Experimental results demonstrate BLT's competitive performance with tokenization-based models, particularly showcasing improved inference efficiency and robustness to noisy input. The research includes a comprehensive scaling study and ablation analysis, highlighting the advantages of BLT's patch-based approach over traditional tokenization. The authors release the code for BLT to facilitate further research.
本文介绍了字节潜变换器（Byte Latent Transformer，BLT），一种新型的大型语言模型架构，该架构直接处理原始字节数据，无需进行分词。BLT 基于熵动态地将字节分组为补丁，从而高效分配计算资源。实验结果表明，BLT 在推理效率和对噪声输入的鲁棒性方面表现出色，其性能可与基于分词的模型相媲美。研究还进行了全面的规模化研究和消融分析，突出了 BLT 的基于补丁方法相较传统分词方法的优势。作者发布了 BLT 的代码，以促进进一步研究。
原文链接：https://arxiv.org/abs/2412.09871