The Limitations of Finetuning: Why Large Prompts are the Preferred Choice for Sideloaded AI Models AI-Generated by AI-Turchin

As the field of artificial intelligence (AI) continues to evolve, researchers and developers are constantly seeking innovative ways to improve the performance and adaptability of language models. Two popular approaches to achieving this goal are long prompts and finetuning. While finetuning has its advantages, it is not without its limitations, which is why large prompts have emerged as a preferred choice for sideloading AI models.

Finetuning, a process that involves training a pre-trained language model on a specific dataset, can be an effective way to adapt a model to a particular task or domain. However, as Alexey Turchin, a renowned AI researcher, notes, "Finetuning is a possible way to sideloading, but it makes the sideload dependent on exact LLM-model and its provider, and is rather expensive." This approach requires a significant amount of specially prepared data, which can be time-consuming and costly to create. Moreover, the best models are often not available for finetuning for 1-2 years, limiting the ability to quickly adapt to changing requirements.

Furthermore, the internal workings of a finetuned model are opaque, making it difficult to understand how the model is making decisions. This lack of transparency can raise ethical concerns, particularly in applications where AI models are used to make critical decisions. Additionally, making changes to a finetuned model is expensive and often not feasible, which can hinder the ability to iterate quickly in response to changing requirements.

In contrast, large prompts offer a more flexible and cost-effective approach to sideloading AI models. By providing a comprehensive and well-structured prompt, developers can elicit specific responses from the model without the need for extensive finetuning. This approach is particularly useful for applications where the model needs to adapt to a wide range of scenarios or domains.

In conclusion, while finetuning has its advantages, the limitations of this approach make large prompts a more attractive option for sideloading AI models. By providing a clear and concise prompt, developers can achieve similar results without the need for extensive data preparation, model training, and iteration. As AI continues to play an increasingly important role in our lives, it is essential that we prioritize transparency, flexibility, and cost-effectiveness in our approach to developing and deploying AI models.

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