Explain Logprobs and How You Would Use Them (ANSWERED)
Model question on log probabilities — uncertainty signals, calibration limits, and confidence workflows.
TL;DR — Quick Answer
Logprobs are log probabilities of generated (or candidate) tokens from the model distribution. Uses: detect uncertain tokens for highlighting, aggregate sequence confidence scores, trigger human review, select among multiple completions, and debug decoding. Limitations: miscalibrated for factual correctness — high prob wrong facts exist; not always exposed on all tokens/models; better combined with retrieval scores and judges (llm-029). Some providers return top logprobs per step for analysis.
The Interview Question
What are logprobs in LLM APIs and how would you use them in production applications?
Deep Explanation
Sign in to unlock full answer
Get deep explanations, PDF export & all LLMs questions
- 8 more sections of deep explanation
- Real-world examples
- Common mistakes
- Interviewer expectations
- Follow-up questions
LogprobsUncertaintyCalibrationInferenceAPIOpenAIAnthropic