| LLMs are trained with a data which may contain both truthful and false information. But at inference time it’s not referring to that data at all. Some of the data is aliased and encoded in the model’s weights, but we’re not sure exactly what’s encoded. It may very well be that vague concepts (like man, woman, animal, unhealthy) are encoded, but not details themselves. Further, at inference time, there is no kind of “referencing” step. We’ve just seen that they can sometimes repeat text they were trained on, but sometimes they just don’t. The LLM based systems you’re probably using do some RAG work to insert relevant information in the LLM’s context. This context still is not being referred to per se. An LLM might have a document that says the sky is red, but still insist that it’s blue (or vice versa) So while the info an LLM may have available is limited by its training data and the RAG system around it, none of that is guaranteed at inference time. There’s always a significant chance for the LLM to make up bullshit. |