[LG] Physics of Language Models: Part 2.1, Grade-School Math and the Hidden Reasoning Process O网页链接 通过可控合成小学数学问题数据集以及模型输出和探测分析,原理性地研究了语言模型如何获得解决这类问题的推理技能,发现了它学会人类相关技能和超越人类推理技能的证据,并指出模型深度对推理长度至关重要。 ...
2.2、知识提取(Part 3.1) 本节内容从视频00:11:37开始 正确的知识提取能力需要在预训练阶段进行学习,而无法仅通过微调阶段来完成。这意味着在预训练阶段必须增加针对知识提取任务的数据增强(Data Augmentation)才能实现。该结论适用于各种模型大小、架构、微调方式、训练方式和超参数。幸运的是,并不需要对所有数据都进...
Physics of Language Models, Part 3, Knowledgemp.weixin.qq.com/s/ZdNZxOajTyHH6PuwSB5HFA?token=1364753235 =zh_CN lumosity:LLM: Physics of Language Models, part 2, Grade-School Math2 赞同 · 0 评论文章 Physics of Language Models, Part 3, Knowledge Part 3.1, Knowledge Storage and Extract...
Language models have demonstrated remarkable performance in solving reasoning tasks; however, even the strongest models still occasionally make reasoning mistakes. Recently, there has been active research aimed at improving reasoning accuracy, particularly by using pretrained language models to "self-correct...
In this week in AI research, OpenAI’s latest models impress in some STEM related tasks, especially in coding. Math is another strong point. In addition, Salesforce is making good on its promise to base its company on AI “agents” – autonomous entities handling customer service and scheduli...
The following guidance refers only to the writing process, and not to the use of AI tools to analyse and draw insights from data as part of the research process: Generative AI and AI-assisted technologies should only be used in the writing process to improve the readability and language of ...
The pertinence of stochasticity is also discussed in the context of the question of how many bits of useful information are contained in the numerical representations of variables, a question that is critical for the design of next-generation climate models. The accuracy of fluid simulation may be...
Atoms and Nuclei: Exploring the structure of atoms and the properties of atomic nuclei, including atomic models, Bohr's model, atomic orbitals, electron configurations, radioactivity, nuclear reactions, and applications to nuclear power and medicine. Electronic Devices: Understanding the principles and ...
结果8:量化为 int8 不会影响模型容量(即使对于处于 2 比特/参数边界的模型也是如此);然而,量化为 int4 会将容量降低到 0.7 比特/参数。 备注1.5. 由于 int8 是 8 位,大型语言模型可以超过理论存储知识限制的 1/4;因此,知识必须在模型的所有层中非常紧凑地存储。
Traditional data-driven deep learning models often struggle with high training costs, error accumulation, and poor generalizability in complex physical processes. Physics-informed deep learning (PiDL) addresses these challenges by incorporating physical