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今天的主题是：Large Language Diffusion Models

Summary

The provided document introduces LLaDA, a novel language model that utilizes a diffusion process rather than the conventional autoregressive method. This work challenges the long-held belief that autoregressive modeling is the only path to creating effective large language models. LLaDA operates by learning to predict masked tokens through a forward masking and reverse generation process, demonstrating competitive performance with established models like LLaMA3 in various tasks, including in-context learning and instruction following. Notably, LLaDA shows strength in handling reversal reasoning, outperforming even GPT-4o in a specific poem completion task. The research suggests that diffusion models offer a promising and viable alternative for the future development of large language models.

这篇文档介绍了LLaDA，一种新型语言模型，它采用了扩散过程，而非传统的自回归方法。这一研究挑战了长期以来的观点，即自回归建模是构建有效大型语言模型的唯一路径。

LLaDA通过学习通过前向掩蔽和反向生成过程来预测掩蔽的token，展现了与现有模型（如LLaMA3）在多项任务上的竞争力，包括上下文学习和指令跟随。值得注意的是，LLaDA在反向推理任务上表现出色，在一个特定的诗歌完成任务中，甚至超过了GPT-4。

该研究表明，扩散模型为未来大型语言模型的开发提供了一个有前景且可行的替代方案。

原文链接：https://arxiv.org/abs/2502.09992