大多数LLMs都具备基本的推理能力,能够回答像“如果一列火车以每小时60英里的速度行驶3小时,它能走多远?”这样的问题。所以,当我们提到推理模型时,指的是那些在更复杂的推理任务(如解决谜题、谜语和数学证明)中表现出色的LLMs。 此外,现在大多数被称为推...
其次,一些推理LLM,如OpenAI的o1,运行多次迭代,中间步骤不显示给用户。 推理”在两个不同的层次上使用:1)处理输入并通过多个中间步骤生成;2)提供某种推理作为对用户的响应的一部分。 When should we use reasoning models? 现在我们已经定义了推理模型,我们可以继续讨论更有趣的部分:如何为推理任务构建和改进LLM。
构建和改进推理模型的四种主要方法 (Four Main Methods for Building and Refining Reasoning Models) 在本节中,我将概述目前用于增强LLM推理能力和构建专门推理模型(如DeepSeek-R1、OpenAI的o1和o3等)的关键技术。 注意:o1和o3的具体工作原理在OpenAI之外仍然未知。不过据推测,它们利用了推理和训练技术的组合。 1. ...
Large language models (LLMs) are at the forefront of AI innovation, enabling the development of advanced generative AI tools that can be applied across various industries. The discussion explores how these models function, focusing on the technical prerequisites for deploying LLMs into production. Sp...
DeepSeek R1's real-time reasoning can be characterized by two modes: A. Implicit Multi-path Generation and Selection -Generation: The model may implicitly generate multiple potential reasoning paths (CoT+Answers) during a single inference but outputs only one. ...
A particular problem with Mixture of Experts is that they are hard to fine-tune. MoEs are very prone to overfitting. After fine-tuning, they are bad at reasoning tasks, but still good at knowledge tasks. A way to mitigate this is to reduce the number of experts, as fewer experts lead...
Understanding domain-specific theorems often requires more than just text-based reasoning; effective communication through structured visual explanations is crucial for deeper comprehension. While large language models (LLMs) demonstrate strong performance in text-based theorem reasoning, their ability to ...
Understanding and Patching Compositional Reasoning in LLMs [ACL 2024] [2024.2] [reasoning] Do Large Language Models Latently Perform Multi-Hop Reasoning? [ACL 2024] [2024.2] [knowledge] [reasoning] Long-form evaluation of model editing [NAACL 2024] [2024.2] [model editing] ...
platform that provides developers with tools to create and deploy applications efficiently, powered by large language models. It includes a flexible range of open-source modules and pre-built sequences that significantly reduce the time and effort required to develop context-aware reasoningAI ...
【LLM表格理解】CHAIN-OF-TABLE: Evolving Tables in the Reasoning Chain for Table Understanding 中二病没有蛀牙 Agent/RL/LLM Post-training 3 人赞同了该文章 这篇论文探讨了如何使用大型语言模型(LLMs)进行表格理解任务,例如基于表格的问题回答和事实验证。 论文的主要贡献是提出了一个名为 CHAIN-OF-TABLE 的...