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How to Reliably Gauge LLM Confidence?
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I’m trying to estimate an LLM’s confidence in its answers in a way that correlates with correctness. Self-reported confidence is often misleading, and raw token probabilities mostly reflect fluency rather than truth.
I don’t have grounding options like RAG, human feedback, or online search, so I’m looking for approaches that work in this constraint.
What techniques have you found effective—entropy-based signals, calibration (temperature scaling), self-evaluation, or others? Any best practices for making confidence scores actionable?
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hi its-mirzabaig This is a really good (and very real) question, and you’re absolutely right to be skeptical of self-reported confidence and raw token probabilities. Most of us run into the same issue once we try to operationalize “confidence” instead of just generating text.
Given your constraints (no RAG, no search, no human feedback), the short answer is: there’s no single reliable signal, but a combination of techniques can get you closer to something that correlates with correctness rather than fluency.
Here are the approaches that tend to work best in practice:
1.Entropy / Log-Probability Signals (with caveats)
Token-level entropy or average log-probability can be useful, but only comparatively:
Lower entropy often means the model is more certain, not more correct.
This works best when you compare multiple answers to the same question or across similar questions.
Treat it as a relative confidence signal, not an absolute truth score.
Best practice: normalize entropy across prompts and combine it with other signals rather than using it alone.
2.Self-Consistency (Surprisingly Effective)
Instead of asking the model once, ask it multiple times with:
Different temperatures
Slight prompt variations
Then measure:
Answer agreement
Semantic similarity between outputs
If multiple independent generations converge on the same answer, confidence tends to correlate better with correctness.
This is often the strongest signal you can get without grounding
Structured Self-Evaluation (Not Just “How Confident Are You?”)
Simple “rate your confidence” questions don’t work well, but structured evaluation does better:
Ask the model to list assumptions
Ask it to identify potential failure points
Ask it to generate counterarguments to its own answer
If the model:
Produces consistent assumptions
Cannot find strong counterexamples
…it’s usually more reliable.
4.Calibration via Temperature Scaling (Offline)
If you have a validation dataset:
Collect entropy / agreement signals
Compare them against actual correctness
Fit a lightweight calibration layer (e.g., temperature or logistic regression)
This doesn’t improve the model—but it makes confidence scores actionable.
5.Refusal & Hedging Detection
Models often hedge (“might”, “possibly”, “depends”) when uncertain.
Detect linguistic uncertainty markers
Combine them with entropy or self-consistency scores
This works well as a negative confidence signal.
What Works Best in Practice
Most teams end up with a composite confidence score, something like:
Self-consistency agreement (high weight)
Normalized entropy (medium weight)
Structured self-critique quality (medium weight)
Hedging detection (penalty)
It’s not perfect—but it’s far more reliable than any single metric.
Without grounding, you’re not measuring truth—you’re measuring internal model stability. The closer your signal reflects stability across perturbations, the closer it tends to correlate with correctness.
Hope this helps—and you’re definitely asking the right question here.
