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No—not reliably on its own. A model’s stated confidence can provide a fallible clue about whether an answer is correct in some tested question-answering settings, but it does not verify that the model read a passage correctly or caught a missed detail. For important answers, check the answer against the source text.
What a confidence score can—and cannot—tell you
Confidence and correctness are related, but they are not the same measurement. A model can report confidence, or produce probabilities associated with answer choices; neither is automatically proof that it noticed a qualifier, understood a sentence, or used the right passage detail.
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Researchers commonly distinguish two properties. Calibration asks whether confidence values correspond to observed accuracy across a set of predictions: for example, whether answers assigned a particular confidence level are correct at a corresponding rate. Discrimination asks whether a confidence signal tends to rank correct answers above incorrect ones. A signal may help distinguish more-likely-correct answers without being a dependable probability for an individual answer.
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Neither measure, by itself, establishes detection of a specific reading mistake. To claim that confidence detects misread qualifiers or missed facts, an evaluation would need to test those errors directly.
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What studies of language-model confidence found
2024: confidence and explanation-based judgments
A 2024 Nature Machine Intelligence study compared model confidence with people’s judgments of answer correctness after they read model explanations. The authors reported model-confidence 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. AUC describes how well a signal separates correct from incorrect answers in the evaluated cases; it is not the probability that a particular answer is correct. Read the study.
In the same study, participants’ 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. These figures concern participants interpreting explanations, not a direct test of whether a model recognizes its own reading errors. The authors also reported that aligning explanation style with model confidence improved participants’ calibration and discrimination in the selected experiments, but did not improve their answer accuracy.
2021: probabilities were not well calibrated in tested tasks
A 2021 Transactions of the Association for Computational Linguistics paper evaluated T5, BART, and GPT-2 on question-answering tasks and found that their probabilities were not well calibrated in the settings studied. Its question—“How can we know when language models know, with confidence, the answer to a particular query?”—frames the problem of relating confidence to correctness; it does not establish a method for catching every reading mistake. The results apply to the models and tasks evaluated then, not automatically to current models. Read the paper.
2022: calibration methods are not universal error detectors
A 2022 EMNLP paper examined confidence-estimation methods involving explanations. It shows that researchers have explored structured approaches to confidence, but it does not establish that a particular method reliably detects all reading errors. Read the paper.
Why a confident answer can still misread the source
The studies examine confidence and answer correctness in particular question-answering settings. They do not show that a model can inspect its own reading process and reliably notice that it overlooked a word such as “not,” missed a condition, or confused two similar details. A model’s explanation can sound plausible without demonstrating that its answer follows from the passage.
Scope matters: results depend on the model, dataset, question format, confidence measure, and evaluation method. A verbal self-rating, answer-choice probability, and calibrated score are different kinds of signals. Evidence that one signal distinguishes correct from incorrect answers on one benchmark does not establish the same performance on another task—or on every category of reading error.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How to check an answer when the passage matters
For a consequential claim, treat confidence as a prompt to investigate, not a substitute for checking the source. A practical check is to ask for the exact passage supporting the answer, then read it yourself and confirm that it entails the answer, including any qualifiers, exceptions, dates, and quantities. This is a reasoned precaution, not a guarantee that asking for a passage or using a particular prompt will eliminate errors.
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- Compare the answer with the original text, not only with the model’s explanation.
- Check whether the quoted or identified passage supports the whole claim rather than just a related detail.
- Pay special attention to negation, conditions, comparisons, and scope limits.
- If the answer affects a decision, seek confirmation from an appropriate reliable source.
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