Why does it happen even after training the model on your knowledge base or even after fine-tuning?
The answer lies in understanding the fundamental structure of an LLM and how it works.
One of the biggest misconceptions is in thinking that LLMs have knowledge or that they are programs.
At their core, they are a Statistical Representation of Knowledge, and understanding this can be profound.
Here is the crucial difference between both.
When you ask a knowledge base a question, it simply looks up the information and spits it out.
Conversely, an LLM is a probabilistic model of knowledge bases that generates answers; hence, it is a Generative Large Language Model. It generates responses based on language probabilities of what word should come next.
As a result, this can lead to hallucinations, self-contradictions, bias, and incorrect responses.
Now, bias goes far deeper than just LLMs, and I’ll cover that ...
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