Why Using LLMs for Confidence Scoring Is a Bad Idea

In AI application development, having a large language model (LLM) provide a confidence score for its own answer seems like an elegant solution: the model both generates the answer and assesses its reliability. However, a discussion on Hacker News points out that this approach has fundamental flaws.

Why Is Confidence Scoring So Tempting?

When building LLM-based applications, developers often need to judge how trustworthy the model's output is. Whether building a customer service bot, a medical consultation assistant, or a code generation tool, knowing "how certain the model is" seems to help the system decide whether to accept the answer or escalate to a human.

Intuitively, having the LLM produce a confidence score from 0 to 1 is the most direct approach. The model has already understood the question, so asking it to evaluate its own answer seems only natural. But the problem lies precisely behind this intuition.

The Fundamental Flaws of LLM Confidence Scoring

1. Models Lack True "Self-Awareness"

LLMs are essentially systems that generate text based on probability distributions; they have no introspective mechanism to accurately determine whether they "know" a given fact. When a model outputs a confidence score, that score is itself generated text—no more reliable than the answer itself.

In other words, a model can confidently give a wrong answer while assigning a high score, or answer correctly yet assign a low score. This inconsistency stems from the model's training objective—predicting the next word, not calibrating uncertainty.

2. Calibration Problems Are Widespread

Research shows that LLM confidence scores are often poorly calibrated. Models tend to be overconfident, especially in domains where training data coverage is insufficient. A model might assign a 0.9 confidence to a question it has never seen, simply because it learned during training to "sound certain when answering."

This overconfidence is dangerous in critical applications. If a system relies on confidence scores to decide whether to escalate a question to a human, low-quality automated answers may be let through because of inflated scores.

Calibration is a core concept in probabilistic forecasting, referring to how well a model's stated confidence matches its actual accuracy. A perfectly calibrated model should be exactly right in 80% of all samples where it claims "confidence 0.8." Common metrics for measuring calibration include Expected Calibration Error (ECE) and the Reliability Diagram: the latter buckets predicted probabilities and compares the average confidence within each bucket against the true accuracy—the closer the curve is to the diagonal, the better the calibration. LLM calibration problems have structural roots: the pretraining objective is to minimize the cross-entropy of the next word, not to output trustworthy probability estimates; RLHF alignment further amplifies the tendency to express "apparent certainty." In addition, when models face out-of-distribution questions, their internal probability distributions are often too flat or too sharp, causing systematically inflated confidence. This explains why simply reading the model's self-reported score is hard to use as a basis for decision-making.

3. Circular Dependency Between Score and Answer

When an LLM is asked to evaluate its own output, the scoring process shares the same model parameters and knowledge gaps as answer generation. If the model holds a mistaken belief about some fact, it will likewise underestimate that error when scoring. This circular dependency prevents confidence scoring from providing a verification signal independent of the answer.

More Reliable Alternatives

Use External Verification Mechanisms

Rather than relying on model self-assessment, introduce external signals. For factual questions, for example, retrieval-augmented generation (RAG) can compare answers against authoritative sources; for code generation, test cases can be run to verify correctness. These methods provide objective feedback independent of the model's judgment.

Retrieval-Augmented Generation (RAG) is a technical architecture that combines external knowledge base retrieval with LLM generation: the system first retrieves relevant snippets from a document library based on the user's question, then injects these snippets as context into the prompt, and the model answers based on the retrieved evidence. In confidence assessment scenarios, the value of RAG lies in providing a "factual anchor" independent of model parameters—the model's answer can be compared against retrieved authoritative text via entailment judgment or fact comparison; if the answer cannot be supported by the evidence, it is deemed low-confidence. By contrast, running test cases to verify code is a form of execution-based verification, whose feedback comes from the actual results of program execution, completely decoupled from any subjective judgment of the model. What both approaches share is the introduction of information sources outside the model, thereby breaking the closed loop of "model self-assessment" and providing traceable, auditable grounds for confidence.

Multi-Model Cross-Validation

Have multiple different models answer the same question separately and compare the consistency of their answers. If several models give similar answers, confidence is naturally higher; if there is clear disagreement, it suggests the question may be uncertain. This approach is more costly, but it avoids the trap of a single model evaluating itself.

Logprob-Based Confidence Estimation

Some APIs provide token-level log probabilities (logprob), from which statistical confidence for an answer can be computed. Although this is not perfect calibration either, it at least reflects the true probability distribution during the model's generation process rather than a regenerated score.

logprob (log probability) is the natural logarithm of the raw probability a language model outputs for each candidate token. For example, a token with probability 0.9 has a logprob of about -0.105; a probability of 0.01 gives about -4.605—the closer the value is to 0, the more confident the model is. Unlike asking the model to "write another confidence number," logprob is distributional information produced directly during the model's forward computation, without secondary generation, and thus better reflects the model's true uncertainty when generating that answer. In practice, you can average or geometrically average the logprobs of the tokens in an answer to obtain a sequence-level confidence; you can also compute the answer's perplexity, i.e., the exponential of the mean logprob, as an overall measure of certainty. Note that logprob measures "how fluently this token was generated," not "how correct this fact is"—a model can easily and with high probability generate a false statement, so it remains a relative signal rather than an absolute guarantee.

Trade-offs in Practice

The Hacker News discussion also reminds us that completely dismissing the value of LLM confidence scoring may be too absolute. In low-risk scenarios, such as content recommendation or draft generation, model self-reported confidence can serve as a rough reference signal. But in high-stakes decisions involving safety, healthcare, or finance, stricter verification processes must be introduced.

The key is to understand the nature of confidence scoring: it is only a hint, not a guarantee. Developers should treat it as one of many signals and use it in combination with other verification methods.

Conclusion

The core reason using LLMs for confidence scoring is a bad idea is that models cannot reliably assess their own uncertainty. This self-assessment mechanism seems convenient but can bring a false sense of security. A more robust approach is to establish external, independent verification paths, freeing confidence assessment from the model's own knowledge limitations.

For AI application developers, this is a reminder: while pursuing automation, don't forget to keep verifiable anchors for critical decisions.

One-line Summary

LLM self-assessed confidence is unreliable due to lack of introspection, poor calibration, and circular dependency; use external verification instead.

Paragraph Summary

Based on a Hacker News discussion, the article analyzes why the common practice of having large language models assign confidence scores to their own answers has fundamental flaws. The author identifies three core problems: LLMs are essentially probabilistic text generation systems without true introspection, so their output scores are just another piece of generated text; model confidence is generally poorly calibrated and tends toward overconfidence, especially in domains with insufficient training data; and self-assessment shares the same parameters and knowledge gaps as answer generation, creating a circular dependency that cannot provide an independent verification signal. The article then offers more reliable alternatives, including external verification mechanisms such as RAG-based fact comparison and running test cases, multi-model cross-validation, and token-level logprob-based statistical confidence estimation. Finally, it emphasizes that confidence should be treated as a hint rather than a guarantee: low-risk scenarios may reference it cautiously, while high-stakes decisions must introduce external verification anchors.