# Irrelevant Context Distraction

> 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.

- Identifier: PTL-0173
- Category: Failure Modes & Evaluation
- Canonical URL: https://protologue.com/t/irrelevant-context-distraction/
- Also known as: distractibility, GSM-IC
- Introduced: 2023

## 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.

## Related terms

- [System 2 Attention](https://protologue.com/t/system-2-attention/)
- [Retrieval-Augmented Generation](https://protologue.com/t/retrieval-augmented-generation/)
- [Context Rot](https://protologue.com/t/context-rot/)
- [Prompt Sensitivity](https://protologue.com/t/prompt-sensitivity/)

## Sources

- Shi et al. (2023). Large Language Models Can Be Easily Distracted by Irrelevant Context. https://arxiv.org/abs/2302.00093

## Cite this entry

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/

License: CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/)