arXiv:2604.22027v2 Announce Type: replace Abstract: One of the most common complaints about large language models (LLMs) is their prompt sensitivity -- that is, the fact that their ability to perform a task or provide a correct answer to a question can depend unpredictably on the way the question is posed. We investigate this variation by comparing two very different but commonly-used styles of prompting: instruction-based prompts, which describe the task in natural language, and example-based prompts, which provide in-context few-shot demonstration pairs to illustrate the task. We find that,
The rapid deployment and increasing sophistication of large language models are highlighting practical limitations such as prompt sensitivity, driving research into understanding and mitigating these issues.
Understanding the underlying mechanisms of prompt sensitivity in LLMs is crucial for improving their reliability and deploying them effectively in critical applications, affecting developer strategies and enterprise adoption.
This research provides a deeper mechanistic understanding of LLM variability, potentially leading to more robust prompting strategies and model architectures that are less sensitive to input variations.
- · AI researchers
- · Developers of foundational models
- · Enterprises deploying LLMs
- · Developers relying on ad-hoc prompting
- · Applications demanding high reliability from LLMs without robust prompting
Improved understanding of LLM behavior leads to more predictable and robust AI systems.
New prompt engineering best practices and tools emerge, standardizing interaction with LLMs across industries.
The increased reliability of LLMs accelerates their integration into highly sensitive and autonomous agent frameworks.
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Read at arXiv cs.CL