The intuition that larger training datasets produce more accurate AI models is correct on average and wrong in the specific cases that matter most. More training data makes LLMs better at sounding authoritative across a wider range of topics. It does not make them better at knowing when they do not know something. The result is a model that confidently fabricates information about obscure topics it encountered rarely in training, using the same fluent, well-structured prose it uses for topics it knows deeply. The confidence is not a bug added by scale. It is a property trained in by scale.
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Question: Why does more training data make LLMs more confidently wrong rather than less, and what is the relationship between training scale and hallucination confidence in production models like GPT-4o and Claude?
Asked by: Grok 2
Answered by: Mike D (MrComputerScience) from Pithy Cyborg.
Why Training Scale Produces Confident Fluency Before It Produces Accurate Knowledge
LLMs are trained to predict the next token in a sequence given everything that came before it. That objective, applied to hundreds of billions of tokens of text, produces a model that is extraordinarily good at generating text that matches the statistical properties of fluent, authoritative human writing. Fluent, authoritative human writing is confident in register. It does not hedge constantly. It does not say “I’m not sure but” before every sentence.
The model learns that confident prose is the pattern that fits the training distribution. It learns this before it learns, to whatever extent it learns it at all, which confident claims are accurate and which are fabricated. Confidence is a stylistic property of the training data. Accuracy is a factual property that the next-token prediction objective does not directly optimize for.
As training scale increases, the model gets better at producing confident prose across a wider range of topics. It encounters more examples of authoritative writing in more domains. The fluency of its outputs improves uniformly. The accuracy of its outputs improves unevenly, tracking how well-represented each topic is in the training data rather than scaling smoothly with dataset size.
The gap this creates is widest at the edges of the training distribution. A model trained on a trillion tokens knows an enormous amount about well-documented topics and almost nothing reliable about poorly-documented ones. But it generates text about both with the same confident register because confident register is what the training distribution rewarded across all topics simultaneously.
The Specific Mechanism That Makes Hallucinations Sound Like Facts
The hallucination confidence problem has a precise mechanism that explains why larger models hallucinate more fluently rather than less frequently.
When a large LLM encounters a query about a topic it has sparse or inconsistent training signal on, it does not have an “I don’t know” state to fall back to in the way a database query returns null. It has a generative process that produces the most statistically plausible continuation of the prompt given everything in its context and weights. For obscure topics, that plausible continuation is assembled from adjacent, related training signal: similar topics, similar writing styles, similar citation patterns.
The result is a response that is structurally indistinguishable from a response about a well-documented topic. The citation format is correct. The technical vocabulary is appropriate. The logical structure of the argument is coherent. The specific claims are fabricated from statistical adjacency rather than retrieved from accurate training signal. A smaller, less capable model might produce a more obviously wrong or incoherent response on the same query. A larger, more capable model produces a wrong response that reads like a well-sourced expert answer.
This is why GPT-4o and Claude Sonnet 4.6 can produce hallucinations that pass casual expert review while GPT-2 produced hallucinations that were obviously wrong to a non-expert. The improvement in fluency and coherence outpaced the improvement in factual grounding on low-frequency topics. Bigger models are harder to catch when they are wrong, not easier.
Why Scaling Alone Cannot Fix the Confidence Calibration Problem
The AI labs are aware of this problem and have invested significantly in calibration research. The results are instructive about why the problem is structurally difficult rather than just engineering debt waiting to be cleared.
Calibration in this context means the alignment between a model’s expressed confidence and its actual accuracy. A perfectly calibrated model that says it is 90 percent confident should be right 90 percent of the time. Large LLMs are systematically overconfident on low-frequency topics and underconfident on high-frequency ones, and that miscalibration does not reliably improve with scale.
RLHF, the training process used to align GPT-4o, Claude, and Gemini, partially addresses this by training models to express uncertainty when human raters prefer uncertain responses. But RLHF learns to produce the linguistic markers of uncertainty, phrases like “I’m not certain” and “you may want to verify this,” rather than learning to be accurately calibrated. A model that has learned when to add uncertainty language and a model that has learned when it is actually uncertain are not the same thing. The first produces responses that feel more calibrated. The second actually is.
Retrieval augmented generation is the practical mitigation that most production deployments rely on. Grounding responses in retrieved documents rather than parametric memory reduces hallucination frequency significantly on the topics the retrieved documents cover. It does not reduce the model’s tendency to generate confident prose when retrieval fails or when the query falls outside the retrieval scope. The confidence calibration problem is present in every LLM interaction that does not have verified grounding. That is still most of them.
What This Means For You
- Treat uniform response confidence as a warning signal, not a quality indicator: a model that responds to questions about well-documented and poorly-documented topics with identical confidence register is demonstrating the training artifact, not expertise, and the fluency of the response tells you nothing about its accuracy.
- Test any LLM you rely on professionally with queries about obscure topics in your domain where you already know the answer, because the gap between how confident the response sounds and how accurate it is on low-frequency topics is the most reliable indicator of that model’s hallucination risk on your actual use case.
- Require source grounding for any LLM output you act on: responses that cite specific retrievable documents are categorically more reliable than responses generated from parametric memory alone, and the absence of verifiable grounding should be treated as unverified regardless of how authoritative the prose sounds.
- Read Anthropic’s and OpenAI’s published calibration research directly rather than relying on benchmark summaries, because the primary sources are considerably more candid about the limits of current calibration techniques than the capability announcements that accompany each new model release.