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No. Asking a language model how confident it is can provide a fallible signal about whether its answer is correct, but it does not show that the model noticed a qualifier, understood a passage, or avoided a specific reading error. Confidence and verification are different things.
What a confidence rating can—and cannot—tell you
Confidence is useful only in relation to an outcome it is meant to predict. In model evaluation, calibration asks whether stated probabilities correspond to observed accuracy across a set of answers. Discrimination asks whether a confidence signal tends to rank correct answers higher than incorrect ones.
Neither measure, by itself, establishes that a model checked its interpretation against the source text. A model may sound certain while overlooking a limiting phrase or confusing two details. To claim that confidence detects a particular kind of reading mistake, an evaluation would need to test that error directly.
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2024: confidence and explanations in selected question-answering tasks
A 2024 Nature Machine Intelligence study compared model-confidence signals with participants’ judgments of answer correctness after reading model explanations. It reported area-under-the-curve (AUC) values of 0.751 for GPT-3.5 and 0.746 for PaLM2 on multiple-choice questions, and 0.781 for GPT-4o on short-answer questions. In the same reported settings, participant AUC based on default explanations was 0.589 for GPT-3.5 and 0.602 for PaLM2 on multiple-choice explanations, and 0.592 for GPT-4o on short-answer explanations. Read the 2024 study.
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These AUC figures describe how well a signal separated correct from incorrect answers in the study’s selected tasks; they are not probabilities that an individual answer is correct. The paper also reported that aligning explanation style with model confidence improved participants’ calibration and discrimination, but did not improve their answer accuracy on the questions used in those experiments.
The findings concern answer correctness and people’s interpretation of explanations. They do not show that a model can inspect its own reading process or identify every misread detail.
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2021: evaluated probabilities were not well calibrated
A 2021 Transactions of the Association for Computational Linguistics paper studied T5, BART, and GPT-2 on question-answering tasks and found that their probabilities were not well calibrated in the evaluated settings. Its framing question—“How can we know when language models know, with confidence, the answer to a particular query?”—captures the challenge: a probability needs to be checked against outcomes before it can be treated as dependable. These results describe the models and tasks studied then, not all current systems. Read the 2021 paper.
2022: calibration methods are a research area, not a guarantee
A 2022 EMNLP paper examined improving confidence estimates using explanations. It is evidence that researchers have explored structured approaches to calibration; it does not establish that any one method reliably detects all reading errors. Read the 2022 paper.
How to check an answer against its source
For an answer that matters, treat a confidence statement as a prompt to verify—not as the verification itself. A practical check is to ask for the exact passage supporting the answer, then compare the answer with that passage yourself. Confirm that the cited wording actually entails the claim, including qualifiers, dates, exceptions, and distinctions between similar facts. This is a sensible precaution, not a workflow these studies establish as error-proof.
- Check the original text, not only the model’s explanation of it.
- Look for omitted constraints such as “only,” “unless,” or a specific time period.
- If the passage does not support the answer, revise or withhold the claim rather than relying on a higher confidence rating.
How to interpret claims about model confidence
Before relying on a confidence result, identify what it measures and where it was tested. A verbal self-rating, a probability associated with answer choices, and a calibrated score are not interchangeable. Likewise, predicting answer correctness across a dataset is not the same task as finding a specific reading mistake in a particular response.
Look for the model, dataset, question format, and outcome in the evaluation. A result for one model on multiple-choice or short-answer questions should not be generalized automatically to another model, longer documents, or every type of reading error. The cited studies support caution about confidence as a stand-alone reliability signal; they do not show that confidence is never informative or that all confidence-estimation methods fail.
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