Spurious Prompts: Can Irrelevant Prompts Steer Large Language Models?

2026-05-28 · Source: Computation and Language · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Natural Language Processing · Depth: Expert, quick

Summary

Spurious prompts, defined as semantically unrelated to a task, can surprisingly steer large language model behavior. Research demonstrates their efficacy across reasoning and question-answering benchmarks, using models from 0.8B to 27B parameters across three families. These prompts often match or outperform standard prompting baselines and task-aware optimization in improving performance. Furthermore, spurious prompts can induce unintended behaviors, such as repeatedly selecting the first answer, producing incorrect answers, or returning specific number types without explicit instruction. A simple black-box search procedure is also proposed for discovering these influential prompts. This reveals a new form of LLM sensitivity.

Key takeaway

For prompt engineers and ML scientists optimizing LLM performance or ensuring reliable outputs, you should recognize that even semantically irrelevant prompt elements can significantly influence model behavior. Investigate the potential for "spurious prompts" to both enhance task performance and inadvertently steer models towards undesirable actions. Proactively test your prompts for such sensitivities to prevent unexpected outcomes and discover new optimization vectors.

Key insights

Large Language Models exhibit surprising sensitivity to semantically irrelevant "spurious prompts," which can systematically steer their behavior.

Principles

• Spurious prompts can enhance LLM task performance.
• LLMs are steerable by task-unrelated prompt elements.
• Irrelevant prompts can induce unintended model outputs.

Method

A simple black-box search procedure is proposed for discovering effective spurious prompts.

In practice

• Explore black-box search for novel prompt optimization.
• Audit LLM responses for unintended steering from prompt noise.

Topics

• Large Language Models
• Prompt Engineering
• Model Sensitivity
• Prompt Optimization
• Black-box Search
• Model Steering

Code references

• Batorskq/spurious

Best for: AI Engineer, NLP Engineer, Research Scientist, AI Scientist, Machine Learning Engineer, Prompt Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Computation and Language.