Deep Dive: Evaluating LLM Confidence Estimation Methods
Disneyskidney · reddit · 2026-07-16
The article provides a detailed comparison of various methods for evaluating Large Language Model (LLM) confidence, categorized into white-box (requires model weights) and black-box (requires only text or tokens).
Text Methods:
- Verbalized Confidence: Directly asking the model to score itself (0-100), but it is heavily influenced by RLHF and prone to overconfidence, with scores often clustering between 80-100%.
- Linguistic Uncertainty: Counting the frequency of ambiguous words like "possibly" or "I think."
- Reasoning Length: Longer, more verbose answers typically indicate higher uncertainty; this works particularly well for reasoning models.
Token Methods:
- P(Answer): Reading and normalizing the probability of answer tokens, though multi-token answers are tricky to process.
- P(True): Asking the model if its own answer is "correct" and reading the yes/no probability. This method fails for reasoning models because the chain-of-thought prematurely locks in the answer.
Sampling Methods:
- Self-Consistency: Sampling the same prompt multiple times to check answer consistency; lower consistency means lower confidence, but this approach incurs high costs.
Related event: Comprehensive Evaluation of LLM Confidence Estimation Methods(3 posts)→
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