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经过几代的时间使用了五种不

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发表于 2023-11-1 14:31:10 | 显示全部楼层 |阅读模式
This process results in customized tips that are optimized for specific applications. is a solution that uses an evolutionary mechanism to iteratively refine the prompting strategy. is unique in its ability to not only improve prompts but also improve its own prompt enhancements with each new generation. Here's how the evolution scheme works A set of evolution units is generated under the guidance of the LL.M. Each evolution unit contains two solution prompts and one mutation prompt. A binary tournament genetic algorithm is then used to evaluate the fitness of these mutants based on the training set to identify better performing mutants. This loop process keeps returning to step.

Steps ultimately lead to the evolution of prompt solutions from generation to generation. Mutation operators of the same category are used to mutate solution hints and mutation hints. The brilliance of this program is that these mutated prompt solutions gradually become smarter. Mutation hints are crucial here. It provides instructi ons 白俄罗斯手机号码列表  on how to mutate to enhance solution hints. Essentially a self-improving, self-referential system operating within the realm of natural language. Crucially, it does not require complex fine-tuning of neural networks. Instead it generates customized prompts carefully optimized for specific applications.



Preliminary experiments have yielded promising results. Outperforms all other contemporary prompting methods on mathematical logic common sense tasks and language classification including identifying hate speech. Looking ahead is rigorously testing its feasibility in building the entire thought process. This involves exploring prompting strategies that conditionally apply prompts to pave the way for the development of LLM policy pre-programming for engaging in confrontational Socratic dialogue. There are still limitations compared to the scalability of human thought processes. The prompt topology remains fixed primarily to adapt to the prompt content rather than the prompt algorithm itself. Human thinking covers many aspects beyond language, including language.


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