π Prompt engineering is crucial for getting large language models to perform well on tasks.
π€ The authors of the paper propose using large language models to automate the process of generating good prompts.
π By leveraging the language model itself, they aim to improve the accuracy of prompt engineering.
π To generate prompt candidates, input-output pairs are given as examples and variations of the prompt are scored.
π The candidates are generated using a large language model and variations of the prompts are continuously scored.
π Ultimately, the best prompt is selected based on the top score.
π Automatic prompt engineering involves using language models to generate candidate prompts for evaluation and improvement.
𧩠There are two types of prompt templates: forward generation templates and reverse generation templates.
π The automated prompt engineering strategy demonstrates comparable or superior performance to prompts crafted by humans.
π The video discusses the challenges of generating prompts for language tasks in large models.
βοΈ The tasks mentioned in the video are relatively simple, such as finding letters or synonyms, performing arithmetic operations, and conducting sentiment analysis.
π§ The speaker wonders how this prompt engineering approach can be applied to more creative tasks in large language models.
π This video discusses the concept of automatic prompt engineering.
π‘ The speaker highlights the importance of automatic prompt engineering in various creative tasks, such as generating short stories, poems, and style transfer.
π The video concludes with a farewell message and a thank you to the audience.
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