【思维链推理相关文献列表】’A Survey of Chain of Thought Reasoning: Advances, Frontiers and Future - A Survey of Chain of Thought Reasoning: Advances, Frontiers and Future' zchuz GitHub: github.com/...
This repository contains the resources forACL 2024paperNavigate through Enigmatic Labyrinth, A Survey of Chain of Thought Reasoning: Advances, Frontiers and Future For more details, please refer to the paper:A Survey of Chain of Thought Reasoning: Advances, Frontiers and Future. ...
常见实际任务:Programming、Math、各领域ReasoningQA(给定一个场景根据已有条件及知识给出结论) 决策则根据Reasoning结果做出相应动作,并反馈后续的状态。 具体方法及原因 Chain of Though 最常用的instruction: Chain of Though 思维链,通过把一个复杂问题拆解分治为一步步的子问题,来获得更准确回复 左侧是传统few-shot ...
including financial services. Despite the extensive research into general-domain LLMs, and their immense potential in finance, Financial LLM (FinLLM) research remains limited. This survey provides a comprehensive
Self-consistency improves chain of thought reasoning in language models. arXiv preprint arXiv:2203.11171, 2022. Wang et al. [2023a] Lei Wang, Chen Ma, et al. A survey on large language model based autonomous agents. arXiv preprint arXiv:2308.11432, 2023. Wang et al. [2023b] Lei...
[2022/05] Selection-Inference: Exploiting Large Language Models for Interpretable Logical Reasoning. Antonia Creswell (DeepMind) et al. arXiv. [paper] [2022/03] Self-Consistency Improves Chain of Thought Reasoning in Language Models. Xuezhi Wang (Google Research) et al. arXiv. [paper] [code]...
Before creating a survey, you need to prepare a strategy to maximize the response rate and collect accurate feedback data. You can’t survey randomly at any point. It would only result in skewed feedback and a waste of time. Here are some pointers that will help you to develop a plan ...
While, withthe chain-of-thought(CoT) prompting strategy[33], LLMs can solve such tasks by utilizing the prompting mechanism that involves intermediate reasoning steps for deriving the final answer. This ability is speculated to be potentially obtained by training on code [33, 47]. An empirical...
Fig. 7. A comparative illustration of in-context learning (ICL) and chain-of-thought (CoT) prompting. ICL prompts LLMs with a natural language description, several demonstrations, and a test query. While CoT prompting involves a series of intermediate reasoning steps in prompts. ...
二. Chain-of-thought Chain-of-Thought Prompting Elicits Reasoning in Large Language Models decompose multi-step problems into intermediate steps; provides an interpretable window into the behavior of the model; can be used to any task that humans can solve via language, such as math word problems...