{
  "id": "irrelevant-context-distraction",
  "code": "PTL-0173",
  "term": "Irrelevant Context Distraction",
  "aliases": [
    "distractibility",
    "GSM-IC"
  ],
  "category": "failure-modes",
  "definition": "Irrelevant context distraction is the drop in a model's accuracy when a prompt contains information that is unrelated to the question, such as an extra sentence in a math word problem, which the model wrongly incorporates into its reasoning.",
  "description": "A benchmark built by adding irrelevant sentences to grade-school math problems showed large accuracy drops across prompting methods. Instructing the model to ignore irrelevant information, using self-consistency, and filtering context first, as in System 2 Attention, all reduce the effect. It matters for RAG, where retrieved passages often include off-topic text.",
  "example": null,
  "broader": [],
  "narrower": [],
  "related": [
    "system-2-attention",
    "retrieval-augmented-generation",
    "context-rot",
    "prompt-sensitivity"
  ],
  "introduced": 2023,
  "added": "2026-10-10",
  "sources": [
    {
      "title": "Large Language Models Can Be Easily Distracted by Irrelevant Context",
      "authors": "Shi et al.",
      "year": 2023,
      "url": "https://arxiv.org/abs/2302.00093"
    }
  ],
  "url": "https://protologue.com/t/irrelevant-context-distraction/",
  "citation": "Protologue. (2026). Irrelevant Context Distraction. In Protologue: A Taxonomy of Prompting and LLM Techniques (v1.4.0, PTL-0173). https://protologue.com/t/irrelevant-context-distraction/"
}