{
  "id": "log-probabilities",
  "code": "PTL-0110",
  "term": "Log Probabilities",
  "aliases": [
    "logprobs",
    "token probabilities"
  ],
  "category": "foundations",
  "definition": "Log probabilities (logprobs) are the logarithms of the probabilities a model assigned to each token it generated, and optionally to the most likely alternatives, exposing how confident the model was at each step.",
  "description": "Applications use logprobs to score classification labels, estimate confidence, flag likely hallucinations, rank candidate completions, and build autocomplete. They describe token-level likelihood, not factual correctness, so a fluent but wrong answer can still have high probability. Not every API or model exposes them.",
  "example": null,
  "broader": [],
  "narrower": [],
  "related": [
    "token",
    "confidence-calibration",
    "temperature"
  ],
  "introduced": null,
  "added": "2026-10-10",
  "sources": [
    {
      "title": "Using logprobs",
      "authors": "OpenAI",
      "year": 2023,
      "url": "https://developers.openai.com/cookbook/examples/using_logprobs"
    },
    {
      "title": "Language Models (Mostly) Know What They Know",
      "authors": "Kadavath et al.",
      "year": 2022,
      "url": "https://arxiv.org/abs/2207.05221"
    }
  ],
  "url": "https://protologue.com/t/log-probabilities/",
  "citation": "Protologue. (2026). Log Probabilities. In Protologue: A Taxonomy of Prompting and LLM Techniques (v1.4.0, PTL-0110). https://protologue.com/t/log-probabilities/"
}