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Instead of calculating errors, LLMs are better at doing self-evaluation!
Instead of calculating errors, LLMs are better at doing self-evaluation!
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It's easier to assess the quality of a response than to generate one which enables LLM models to evaluate their own performance.
It's easier to assess the quality of a response than to generate one which enables LLM models to evaluate their own performance.
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It’s like trying to figure out how many ingredients you left out while cooking a recipe but without knowing exactly which ones you missed. LLM models like experienced cooks, can’t always tell you what specific step they skipped but they can guess how close they got to the final dish. For example, if your meal tastes 75%, you know something is off, but you are not sure what exactly.
It’s like trying to figure out how many ingredients you left out while cooking a recipe but without knowing exactly which ones you missed. LLM models like experienced cooks, can’t always tell you what specific step they skipped but they can guess how close they got to the final dish. For example, if your meal tastes 75%, you know something is off, but you are not sure what exactly.
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Now instead of focusing on identifying every missed ingredient, think about just estimating how well the dish turned out overall. It’s easier to guess if the meal tastes good than to pinpoint each small mistake. LLMs do the same, they estimate how well they performed without knowing every single error, allowing them to self-evaluate! 
Now instead of focusing on identifying every missed ingredient, think about just estimating how well the dish turned out overall. It’s easier to guess if the meal tastes good than to pinpoint each small mistake. LLMs do the same, they estimate how well they performed without knowing every single error, allowing them to self-evaluate! 
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resource
https://huggingface.co/meta-llama/Llama-3.2-1B
model
meta-llama/Llama-3.2-1B
https://huggingface.co/meta-llama/Llama-3.2-1B
Instead of calculating errors, LLMs are better at doing self-evaluation!
It's easier to assess the quality of a response than to generate one which enables LLM models to evaluate their own performance.
It’s like trying to figure out how many ingredients you left out while cooking a recipe but without knowing exactly which ones you missed. LLM models like experienced cooks, can’t always tell you what specific step they skipped but they can guess how close they got to the final dish. For example, if your meal tastes 75%, you know something is off, but you are not sure what exactly.
Now instead of focusing on identifying every missed ingredient, think about just estimating how well the dish turned out overall. It’s easier to guess if the meal tastes good than to pinpoint each small mistake. LLMs do the same, they estimate how well they performed without knowing every single error, allowing them to self-evaluate! 

https://huggingface.co/meta-llama/Llama-3.2-1B
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