Large language models suffer from four critical flaws that make them unreliable for high-stakes work: * They confidently state things that are factually incorrect. * They give fluent, certain-sounding answers when honest uncertainty would be more appropriate. * They do not reliably retain corrections. * They struggle with deep, consistent...
Large language models (LLMs) share several fundamental problems which severely constrain the precision, consistency and accuracy of their answers. In brief, they: * say things which are factually incorrect * give fluent and confident answers when honest uncertainty would be more accurate * do not reliably retain corrections * struggle...
Large language models today have a basic problem: they tend to treat information as equally valid if it appears often enough in their training data. They don’t do a good job distinguishing high-quality evidence from low-quality or biased sources. This leads to several recurring issues: * They confidently state things...
Epistemic status: Conceptual architecture proposal developed through extended discussion and self-critique. Implementation details remain speculative. Improving Truth-Seeking in LLMs: An Internal Verified Knowledge Base with Ratcheted Multi-Track Confidence Current large language models are inherently relativistic: they weight evidence by statistical prevalence rather than by quality. This produces four persistent failure...