{
  "id": "few-shot-calibration",
  "code": "PTL-0021",
  "term": "Few-shot Calibration",
  "aliases": [
    "contextual calibration",
    "calibrate before use"
  ],
  "category": "exemplars",
  "definition": "Few-shot calibration corrects a model's systematic biases toward particular answers, such as the most frequent or most recent label in the examples, by adjusting output probabilities measured on a content-free input.",
  "description": "Zhao et al. identified majority-label bias, recency bias, and common-token bias in few-shot prompting, and proposed contextual calibration, which estimates the bias from an input such as \"N/A\" and rescales predictions to neutralize it.",
  "example": null,
  "broader": [
    "few-shot-prompting"
  ],
  "narrower": [],
  "related": [
    "exemplar-ordering",
    "prompt-sensitivity"
  ],
  "introduced": null,
  "sources": [
    {
      "title": "Calibrate Before Use: Improving Few-Shot Performance of Language Models",
      "authors": "Zhao et al.",
      "year": 2021,
      "url": "https://arxiv.org/abs/2102.09690"
    }
  ],
  "url": "https://protologue.com/t/few-shot-calibration/",
  "citation": "Protologue. (2026). Few-shot Calibration. In Protologue: A Taxonomy of Prompting and LLM Techniques (v1.0.0, PTL-0021). https://protologue.com/t/few-shot-calibration/"
}