Do you ever wonder why LLMs Hallucinate or get things completely wrong?
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 in more detail in a future email, but for now, the question is what can be done about all of this and how can we work with LLMs in such a way as to limit bias, hallucinations and incorrect responses?
Here are a few techniques we can use:
- NLU: using NLU for critical areas where a specific answer is required
- Knowledge Bases: Feeding the LLM information that can be used as the basis for answering questions
- Prompt Engineering & Prompt-tunning: This can be used to optimize the performance and accuracy of the model.
- Fine-Tuning: Training the model on your data
Want to go deeper?
We created a free Guide to LLMs that covers the basics and advanced topics like fine-tuning, and we hope to offer a model and framework for optimizing your success with LLMs.
Till next time
🤯 Unlock the Secrets to Reducing LLM Hallucinations was originally published in Chatbots Life on Medium, where people are continuing the conversation by highlighting and responding to this story.