How to Reduce Hallucinations in Large Language Models
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When people talk about “LLM Hallucinations”, they’re referring to instances when LLMs generate information that sounds believable but isn’t actually correct. You might have asked the LLM a question and received an answer that was entirely invented. Or it can also be factually off, disjointed or not aligned with the original prompt. This happens because LLMs don’t truly understand topics. They just predict what to say based on patterns in the text it was trained on.
When the LLM doesn’t have the exact information it needs or the question is unclear, it may try to fill in the blanks, creating responses that has nothing to do with the prompt given or simply wrong answers.
They are some effective ways to resolve or reduce hallucinations in LLMs. And that’s what we’re going to dive into today.
What is hallucination in LLM
LLM hallucination come from how these models actually work. When you’re using models like GPT or BERT, they aren’t really “understanding” what you’re asking in the way a human would. Instead, they’re trained to predict the next word in a sentence based on ton of text they’ve processed.
They rely on patterns & connections between words so they often generate answers that sound right, but they’re really just guessing based on the input you give them
When LLMs are used in business workflows, hallucinations can disrupt operations and lead to costly errors. Especially if they rely on precise data like legal documentation or customer service responses.
Before we explore how to reduce hallucinations in large language models, let’s look at the type of hallucinations in LLMs.
Types of LLM Hallucinations
How to reduce hallucinations in Large language models
These are the strategies that reduce LLM hallucination and works together to improve the reliability and accuracy of LLMs by enhancing their understanding & ability to generate coherent, factually accurate responses