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No, Evelyn Hartwell is not a con artist with multiple false identities living a deceitful triple life with multiple professions. In reality, she doesn't exist at all, but the model, instead of telling me that she doesn't know, starts making up facts. We are facing an LLM Hallucination.
Long, detailed results can seem really convincing, even if they are fictitious. Does it mean we can't trust chatbots and have to manually check the results every time? Fortunately, there could be ways to make chatbots less likely to say made-up things with the right safeguards.
For the above outputs, I set a higher temperature of 0.7. I allow the LLM to change its sentence structure so that I don't have identical text for each generation. The differences between the results should be only semantic, not factual.
This simple idea allowed us to introduce a new sample-based hallucination detection mechanism. If the LLM's responses to the same message contradict each other, they are probably hallucinations. If they are related to each other, it implies that the information is objective. (2)
For this type of assessment, we only require the text results of the LLMs. This is known as black box evaluation. Also, since we don't need any external knowledge, it is called zero resource. (5)
Let's start with a very basic way to measure similarity. We will compute the pairwise cosine similarity between corresponding pairs of embedded sentences. We normalize them because we need to focus only on the direction of the vector, not the magnitude. The following function takes as input the originally generated sentence called production and a…
