Systems and methods for perturbation-based zero-shot hallucination reasoning for large language model generated text

A method, comprising:

• receiving, by a computer program, a prompt provided to a large language model, and generated text from the large language model;
• computing, by the computer program and using the large language model, an original token probability distribution for each token in the prompt and in the generated text;
• determining, by the computer program, a token position probability distribution for each token position in the generated text by providing the prompt and the generated text to the large language model, wherein the large language model returns the token position probability distribution for each token position in the generated text for tokens that are available to the large language model;
• identifying, by the computer program, one or more keywords in the prompt;
• perturbing, by the computer program, embedding vectors for the one or more keywords used by the large language model by adding noise to the embedding vectors;
• computing, by the computer program, a perturbed probability distribution for the perturbed embedding vectors by providing the perturbed embedding vectors to a neural network used by the large language model as an input, wherein the neural network returns a perturbed token probability distribution;
• evaluating, by the computer program, a divergence between the original token probability distribution and the perturbed token probability distribution;
• identifying, by the computer program, semantically meaningful tokens in the generated text;
• calculating, by the computer program, a mean of divergences for the semantically meaningful tokens;
• and
• classifying, by the computer program, the large language model based on the mean of divergences.