The recent advent of large language models has reinvigorated debate over whether human cognitive capacities might emerge in such generic models given sufficient training data. Of particular interest is the ability of these models to reason about novel pr
论文:[2502.21321]LLM Post-Training: A Deep Dive into Reasoning Large Language Models 本调查系统地探讨了后训练方法,分析了它们在预训练之外完善LLMs的作用,解决了诸如灾难性遗忘、奖励劫持和推理时权衡等关键挑战。我们强调了模型对齐、可扩展适应和推理时推理方面的新兴方向,并概述了未来的研究方向。 llm的训练...
论文地址:Towards Reasoning in Large Language Models: A Survey 人类vs. 机器 人类可以处理很多任务,具备小样本学习能力(Few-shot Learning Ability) 人类有能力从一个熟悉的场景泛化到更困难的场景(Out-of-distribution Robustness 鲁棒性) 人类对于自己的决定和预测是可以提供一个解释的,但是机器(尤其是深度神经网络...
NIM LLM supports deploying Reasoning Models designed to generate detailed, step-by-step thought processes. These models are post-trained using two unique system prompts to support two different modes: detailed thinking on (chain-of-thought responses) and detailed thinking off (concise responses). ...
Title: Empowering Large Language Models with Faithful Reasoning 主讲人(Speaker):Liangming Pan 时间(Date & Time):2024.7.5;10:00-11:30 地点(Location):理科一号楼1453(燕园校区) Room 1453, Science Building #1 (Yanyuan) 邀请人(H...
Large Language Models, such as GPT-4o and Claude 3.5 Sonnet, have progressed beyond simple pattern recognition to demonstrate capabilities that mimic human-like reasoning in many aspects. Key Reasoning Capabilities of LLMs: Contextual Understanding: LLMs can grasp complex contexts, enabling m...
With the emergence of advanced reasoning models like OpenAI o3 and DeepSeek-R1, large language models (LLMs) have demonstrated remarkable reasoning capabilities. However, their ability to perform rigorous logical reasoning remains an open question. This survey synthesizes recent advancements in logical ...
0x1:Large Language Models 语言模型(Language Models, LMs)是具有理解和生成人类语言能力的计算模型。LMs具有预测词序列的概率或根据给定输入生成新文本的能力。 N-gram模型是LMs中最常见的类型,它基于前文环境来估计下一词的概率。 然而,LMs也面临着一些挑战,例如罕见或未见词的问题、过拟合问题以及捕捉复杂语言现象...
3.3 ARITHMETIC REASONING 我们发现对比解码倾向于帮助具有思维链提示的算术推理任务;所有结果请参见表 2。这方面的一个例外是 MATH数据集,它被证明对标准解码和对比解码都具有挑战性。我们推测,由于对比解码放大了专家比业余学习更好的技能,它不能帮助超出专家能力的任务。
标题:Chain-of-Thought Prompting Elicits Reasoning in Large Language Models 链接:arxiv.org/pdf/2201.1190 一、简介 自然语言处理领域最近被语言模型(Peters等,2018;Devlin等,2019;Brown等,2020)彻底改变了。通过增加语言模型的规模已经被证明可以带来一系列好处,例如提高性能和样本效率(Kaplan等,2020;Brown等,2020)...