2 Chain-of-Thought (CoT) Decoding 2.1 Pre-trained Language Models Can Reason without Prompting 2.2. CoT-Decoding for Extracting CoT Paths 3. Experiments 4. Related Work 1 Introduction 大语言模型 (LLM) 在各种复杂的推理基准测试中展现了显著的性能。这些 LLMs 的推理能力通常通过提示技术来激发,这些技...
如“yes”和“no”,首先聚合“yes”(以及“Yes”等)对应token的概率得到概率质量,然后计算“yes”和“no”的总概率质量差异得到Δ。 4 Branching at other decoding steps CoT解码在第一个解码步骤中考虑替代token,这引出了一个自然的问题:分支在后续的解码步骤中是否可行?对于解码路径的定性分析表明,第一个解码步...
[CL]《Chain-of-Thought Reasoning Without Prompting》X Wang, D Zhou [Google DeepMind] (2024) http://t.cn/A6Y6N1xu #机器学习##人工智能##论文#
Chain of thought (CoT) prompting has evolved into various innovative variants, each tailored to address specific challenges and enhance the model's reasoning capabilities in unique ways. These adaptations not only extend the applicability of CoT across different domains but also refine the model's pr...
3. Multimodal Chain of Thought Prompting As the name suggests, Multimodal chain of thought prompt leverages the power ofMultimodal AIto integrate input from a wide range of modalities, such as text, images, and audio, to process and integrate a wide range of information for complex reasoning ta...
🔤 Reasoning in Large Language Models -An Emergent Ability Chain of Thought Prompting Elicits Reasoning in Large Language Models.NeurIPS 2022 Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Brian Ichter, Fei Xia, Ed Chi, Quoc Le, Denny Zhou.[Paper] [Blog], 2022.1 ...
Experiments show that inducing a chain of thought via prompting can enable sufficiently large language models to better perform reasoning tasks that otherwise have flat scaling curves. When combined with the 540B parameter PaLM model, chain of thought prompting achieves new state of the art of 58.1...
techniques: Chain-of-Thought (CoT) prompting. This technique involves structuring the prompt in a way that makes it easier for the model to complete complex tasks requiring reasoning or problem-solving. It has been shown that, without CoT, the very same model fails to provide the correct ...
Large language models (LLMs) showcase impressive reasoning capabilities when coupled with Chain-of-Thought (CoT) prompting. However, the robustness of this approach warrants further investigation. In this paper, we introduce a novel scenario termed preemptive answers, where the LLM obtains an answer...
但是也发现,使用cot解码方法只能应对预训练任务中频繁出现的任务,对于复杂的任务还是需要设计prompt触发合理的推理过程。使用小样本prompt更有指导作用。 四、COT解码 4.1解码时cot路径的存在 采用PaLM-2大模型来比较贪心解码路径( = 0),与cot解码路径 ( > 0),其中 表示第一个解码步骤中第 个标记的选择。