The question of whether fluency is (well) correlated to accuracy seems to assume something like mentalizing, the idea that there's a correspondence between minds mediated by a correspondence between the structure of the world and the structure of our minds/language. We've talked about the "interface theory of perception", where Hoffman (I think?) argues we're more likely to learn *false* things than we are true things. And we've argued about realism, pragmatism, prediction coding, and everything else under the sun on this list.
So it doesn't surprise me if most people assume there will be more true statements in the corpus than false statements, at least in domains where there exists a common sense, where the laity *can* perceive the truth. In things like quantum mechanics or whatever, then all bets are off becuase there are probably more false sentences than true ones. If there are more true than false sentences in the corpus, then reinforcement methods like Marcus' only bear a small burden (in lay domains). The implicit fidelity does the lion's share. But in those domains where counter-intuitive facts dominate, the reinforcement does the most work. On 9/9/25 3:12 PM, Marcus Daniels wrote:
Three ways some to mind.. I would guess that OpenAI, Google, Anthropic, and xAI are far more sophisticated.. 1. Add a softmax penalty to the loss that tracks non-factual statements or grammatical constraints. Cross entropy may not understand that some parts of content are more important than others. 2. Change how the beam search works during inference to skip sequences that fail certain predicates – like a lookahead that says “Oh, I can’t say that..” 3. Grade the output, either using human or non-LLM supervision, and re-train. *From:*Friam <[email protected]> *On Behalf Of *Russ Abbott *Sent:* Tuesday, September 9, 2025 3:03 PM *To:* The Friday Morning Applied Complexity Coffee Group <[email protected]> *Subject:* [FRIAM] Hallucinations OpenAI just published a paper on hallucinations <https://cdn.openai.com/pdf/d04913be-3f6f-4d2b-b283-ff432ef4aaa5/why-language-models-hallucinate.pdf> as well as a post summarizing the paper <https://openai.com/index/why-language-models-hallucinate/>. The two of them seem wrong-headed in such a simple and obvious way that I'm surprised the issue they discuss is still alive. The paper and post point out that LLMs are trained to generate fluent language--which they do extraordinarily well. The paper and post also point out that LLMs are not trained to distinguish valid from invalid statements. Given those facts about LLMs, it's not clear why one should expect LLMs to be able to distinguish true statements from false statements--and hence why one should expect to be able to prevent LLMs from hallucinating. In other words, LLMs are built to generate text; they are not built to understand the texts they generate and certainly not to be able to determine whether the texts they generate make factually correct or incorrect statements. Please see my post <https://russabbott.substack.com/p/why-language-models-hallucinate-according> elaborating on this. Why is this not obvious, and why is OpenAI still talking about it?
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