The content discusses the importance of prompting methods for LLMs, introducing the concept of Chain of Thought (CoT) prompting and proposing a new approach called Hint of Thought (HoT) prompting. HoT is designed to improve reasoning tasks by providing an explainable, logical, and end-to-end prompt method. Experimental results demonstrate the effectiveness of HoT in various reasoning tasks, surpassing existing zero-shot methods.
Key points include the significance of scaling up generative language models, the role of zero-shot learning in understanding different tasks, the challenges faced by large-scale models in multi-step reasoning tasks, the introduction of CoT prompting as an alternative to standard question-answer prompts, and the development of HoT as an improved zero-shot prompt method. The paper also presents detailed examples illustrating how HoT works on different datasets and provides insights into error analysis and ablation studies.
Overall, HoT emerges as a promising approach to enhancing reasoning tasks with LLMs through its structured step-by-step prompting methodology.
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by Ioktong Lei,... at arxiv.org 03-01-2024
https://arxiv.org/pdf/2305.11461.pdfDeeper Inquiries