For LLM's no, but the explanation is wrong also, it has nothing to do with 'false memories' and has everything to do with how LLMs work. Here is the paper. ht

For LLM's no, but the explanation is wrong also, it has nothing to do with 'false memories' and has everything to do with how LLMs work. Here is the paper. https://arxiv.org/abs/2401.11817 https://arxiv.org/abs/2401.11817 RAG and fine tuning improve domain specificity and may reduce the problem to a level where you don't care, but it will always be there. Clean data would help reduce the incidents, possibly to a level that is more usable, but also don't remove the problem. Considering next token prediction as serial runs on multi tape TMs, with the previous output as the input can help. Especially if you consider Microsoft's 1.53bitnet that requires full precision for training but can reduce weights to just the sign components for inference. Unfortunately all paths to explain this I have require graduate level complexity theory and/or diff geometry. Or you relive the Brouwer–Hilbert controversy by trying the logic path.