Why You Shouldn't Ask LLMs for Confidence Scores: The Pitfalls of Self-Reported Reliability

A Widely Misused Practice

When building applications powered by large language models (LLMs), developers often want a quantitative reliability metric. An intuitive approach is to simply ask the model for a "confidence score" right in the prompt — something like "Please rate your certainty in this answer from 0 to 100." It's a tempting idea: if the model could tell us how certain it is, we could filter out low-quality outputs, trigger human review, or decide whether to escalate to a more powerful model.

However, a growing number of practitioners and researchers have pointed out that asking an LLM directly for a confidence score is a deeply misleading practice. The number the model produces often fails to reflect the actual probability that its answer is correct. Understanding why this happens is essential for any team serious about building production-grade AI systems.

A Model's Stated Confidence Is Not a Real Probability

The Nature of Generative Output: The Model Is "Composing" a Number, Not "Computing" a Probability

At its core, a large language model is an autoregressive text generator. When you ask it to output "Confidence: 87%," the model is not performing rigorous probabilistic reasoning — it is generating a sequence of text that best fits its training distribution. In other words, "87%" is simply a string of tokens the model considers most likely to appear in the current context, not a genuine measure of its own epistemic state.

To understand this, you need to grasp how autoregressive models fundamentally work: they predict the next most likely token one at a time. At each generation step, the model computes a probability distribution over the vocabulary based on all previously generated tokens, then samples from it or selects the highest-probability token. This means every output — including numbers, punctuation, and so-called "confidence scores" — is a product of the same next-token prediction process. The model has no independent "metacognitive module" that evaluates its own knowledge state; it has only a unified text generation mechanism. When we ask the model to output "87%," that number is mechanistically no different from the model generating "the weather is nice today."

This creates a fundamental mismatch: the confidence a model states measures "does this number look reasonable?" rather than "is my answer correct?" A factually wrong claim that appears frequently in the training data may be expressed with extremely high "confidence," because the model has learned the expression pattern, not verified the facts.

The Calibration Problem: Model Confidence Is Severely Disconnected from Actual Accuracy

In machine learning, a model is considered "well-calibrated" if predictions made with 80% confidence are correct approximately 80% of the time over the long run. Calibration is a core concept in evaluating probabilistic prediction quality, first widely applied in weather forecasting — a perfectly calibrated weather system should see rain on roughly 70% of the days for which it predicted a 70% chance of precipitation. Common metrics for measuring calibration quality include Expected Calibration Error (ECE) and reliability diagrams.

Research has shown that dialogue models fine-tuned with RLHF (Reinforcement Learning from Human Feedback) tend to produce verbally expressed confidence scores that are severely overconfident, with very poor calibration. Models tend to give high scores because "confident, assertive" answers are typically preferred by human annotators during training.

It's worth diving deeper into how the RLHF training mechanism systematically causes this problem. During RLHF, human annotators rank and score multiple model responses, and this preference data is used to train a reward model. A reinforcement learning algorithm (such as PPO) then optimizes the language model to achieve higher reward scores. The issue is that human annotators typically prefer answers that sound certain and confident — a hesitant answer, even if more honest, tends to score lower. This training signal systematically pushes the model toward "appearing confident" rather than "accurately expressing uncertainty." Researchers have also found that pre-trained models often exhibit relatively good calibration at the token-level probability, but after instruction tuning and RLHF, the gap between verbally stated confidence and actual accuracy widens significantly.

This means that an answer a model reports being "95% confident" about may have an actual accuracy rate far below 95%. Making decisions based on such numbers is equivalent to building a system on a false foundation of reliability.

More Reliable Alternatives: How to Scientifically Assess LLM Output Uncertainty

If we can't directly ask the model for a confidence score, how should we evaluate the reliability of its outputs? Here are several validated, more well-grounded approaches.

Method 1: Token-Level Log Probabilities (logprobs)

Many model APIs (such as the OpenAI API) return the log probabilities (logprobs) of generated tokens. These values come directly from the model's softmax output distribution and are much closer to the model's true internal state than asking the model to "verbally report a number."

From a technical perspective, in a Transformer architecture, the model's final layer outputs a logits vector with a dimension equal to the vocabulary size (typically tens of thousands to over a hundred thousand). Applying the softmax function to this vector produces a probability distribution for each token being selected. The log probability is the natural logarithm of this probability — for example, if a token's generation probability is 0.95, its logprob is approximately -0.05; if the probability is 0.01, the logprob is approximately -4.6. The closer the logprob is to 0, the more certain the model is about that token choice. In practice, developers can examine the logprob values of key tokens in the answer (such as entity names or numbers) — if a key token's logprob is very low (large absolute value), it indicates the model was uncertain at that position and the output may need additional verification. The OpenAI API also allows returning the top-k candidate tokens and their logprobs at each position, providing a window into the model's internal "hesitation."

By analyzing the probability distribution of key tokens, you can more objectively estimate uncertainty. It's worth noting that logprobs also require calibration processing to be converted into meaningful probabilities, but they are at least a genuine product of the model's computation process, rather than a piece of generated textual performance.

Method 2: Self-Consistency Sampling

Another widely validated method is multiple sampling. For the same question, use a higher temperature parameter to have the model generate multiple independent answers, then observe the degree of consistency among them:

• High consistency: If the model repeatedly gives the same answer, that answer is more robust and trustworthy
• Low consistency: If the results differ every time, the model is actually in a state of high uncertainty — regardless of how "confident" it claims to be

The theoretical intuition behind this approach comes from ensemble methods: the consensus of multiple independent predictions is usually more reliable than any single prediction. In the LLM context, by setting a higher temperature parameter (e.g., 0.7–1.0), the model explores different reasoning paths with each sample. The temperature parameter controls the "sharpness" of the softmax distribution — the higher the temperature, the flatter the probability distribution and the greater the sampling randomness. A 2022 paper from Google Research first systematically proposed this method and demonstrated significant performance improvements on mathematical reasoning and commonsense question-answering tasks. In practice, sampling 5–20 times typically yields a good consistency estimate, though this requires balancing API call costs and latency.

The core advantage of this method is that it measures uncertainty through the model's behavior, not its self-report.

Method 3: External Verification and Retrieval-Augmented Generation (RAG)

True reliability often requires mechanisms outside the model to ensure quality. Providing traceable evidence for answers through Retrieval-Augmented Generation (RAG), or introducing independent verification models for fact-checking, is far more reliable than depending on the model's self-assessment.

A typical RAG architecture involves three stages: first, converting the user query into a vector representation (usually using a dedicated embedding model); then retrieving the most relevant document fragments from an external knowledge base (using vector similarity search, such as cosine similarity or dot product); and finally injecting the retrieved content as context into the model's prompt. The reliability advantage of this approach is that the model's responses now have traceable supporting evidence — we can verify whether the original documents cited by the model actually support its conclusions. Additionally, RAG helps mitigate the model's "hallucination" problem, because the model is guided to derive answers from given evidence rather than relying entirely on parametric memory that may contain outdated or incorrect information.

The core principle is: subject the model's output to external verification, rather than letting it grade itself.

Implications for System Design

Although this topic may seem narrow, it touches on a pervasive cognitive bias in current LLM application development: we tend to anthropomorphize models, assuming they understand the boundaries of their own knowledge like a human expert would. But in reality, a model's ability to express "I don't know" is itself a trained behavioral pattern, not the result of genuine introspection.

For engineering practice, this means:

• Do not use model self-reported confidence scores in critical decision paths — these numbers lack statistical reliability
• Prefer uncertainty measures with computational grounding, such as logprobs and consistency sampling
• For high-risk scenarios, always introduce external verification or human fallback mechanisms
• When evaluating system reliability, measure actual calibration curves on real datasets rather than trusting the model's self-report

Conclusion

The advice "don't ask LLMs for confidence scores" is rooted in a deep understanding of how generative models work. Large language models excel at producing fluent, plausible text, but fluency does not equal correctness, and confidence does not equal reliability. Building trustworthy AI systems requires us to abandon naive trust in model self-assessment and instead rely on more rigorous, verifiable uncertainty measurement methods. As AI increasingly penetrates mission-critical operations, this disciplined mindset is indispensable.