Code on my github You may have heard that Auto Regressive LLMs (Large Language Models) are not good with certain tasks, given wrong or inaccurate results. Classical examples is the prompt: count the number of r in word strawberry Asking local llama 3.2 to count the number of 'r' on word 'strawberry' and it wrongly says 2 Or which number is bigger: 9.11 or 9.9 Asking Llama 3.2 the prompt which number is bigger: 9.11 or 9.9 and it wrongly says that 9.11 is bigger Of course modern LLMs may be trained to target such cases and may get it right, but if you try similar prompts you may get inaccurate results or even make the LLM "think" that they should answer something else and change the answer to something wrong. But still, there are issues that LLMs simply can't solve due the tokenization process and the context lenght. This is discussed in depth in recent Andrej Karpathy video about LLMs: In this same video Karpathy suggests the use of code to solve such ...
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