What causes an LLM to state a fact incorrectly, and whi...

Factual errors in LLMs originate from five primary sources: the corpus, the query, the model weights, the decoding process, and the reconciliation of information.

Retrieval-Augmented Generation (RAG) is not a panacea. It is effective at fixing errors caused by outdated knowledge or the model’s limited parametric memory. However, it is less effective at fixing over-generalization and can actually introduce new errors when the retrieved documents contain conflicting information or when the query is ambiguous, leading to poor retrieval performance.