LLMs generate responses based on statistical probabilities derived from their training data. They do not inherently understand or store an "absolute source of truth." Thus, any KG bootstrapped from an LLM might inherit not only the model's insights but also its inaccuracies and biases (hallucinations). You need to understand that these hallucinations are not errors of logic but they are artifacts of the model's training on vast, diverse datasets and reflect the statistical patterns in that data.
Maybe you could build retrieval model but not generative model.
LLMs generate responses based on statistical probabilities derived from their training data. They do not inherently understand or store an "absolute source of truth." Thus, any KG bootstrapped from an LLM might inherit not only the model's insights but also its inaccuracies and biases (hallucinations). You need to understand that these hallucinations are not errors of logic but they are artifacts of the model's training on vast, diverse datasets and reflect the statistical patterns in that data.
Maybe you could build retrieval model but not generative model.