Eight Things to Know about Large Language Models.pdf838KB
8 Things You Need to Know About LLMs
It is interesting to use the analogy of the art and science of cooking to understand large-scale language models (LLMs). These models are like sophisticated chefs who work with language as their ingredient, and their cooking skills are improved through 'investment'. This analogy can help us to explain the complex functions of LLMs in a more understandable way.
The 'autocomplete' function of LLMs is similar to a chef predicting the taste of ingredients and cooking them harmoniously. It detects the 'taste' of the given text and adds the 'ingredients' of the next word or sentence that go well together to complete the delicious 'dish'. In this process, LLMs analyzes various language patterns and structures with 'taste' and creates an appropriate 'recipe' through this.
Autocomplete method (advanced)
The 'investment' required for the development of LLMs is like a chef exploring new ingredients, building a bigger kitchen, and investing more time in practice to make better dishes. The elements of data, model size, and computational power correspond to the chef's ingredients, kitchen equipment, and cooking skills. More investment allows the chef to make more diverse and complex dishes.
What is investing in LLM?
However, these 'dishes' have their limitations. The 'dishes' created by LLMs can sometimes taste unexpected and may use the wrong 'ingredients'. This is just like a chef cannot always make a perfect dish.
LLMs are like chefs who cook delicious dishes with the ingredients of language. Their ability is the result of rich 'investment' and explores the various flavors of language through the 'autocomplete' function. However, there is still room for improvement in these dishes, and LLMs, as chefs, must continuously improve to make better dishes. Based on this understanding, the future of LLMs is expected to present richer and more sophisticated dishes of language.
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