Let's investigate the effects of prompt framings on LLM (large language model) performance and safety.
For instance, does a model's performance increase or decrease if it receives a call to urgency before being given a task? Is it an effective strategy to instruct the model to take a deep breath before providing its answer?

Summary

We consider the effect of simple prompt framings on the performance and safety of LLMs. The task of coding profits most strongly from framing. The most reliable form of framing is to instruct the model to take a deep breath before giving its reply, which works well even for recall or logic-based tasks. Prompt framing can also influence a model's safety mechanisms. Expert, scarcity, and reciprocity framing were shown to increase the rate of compliance with harmful requests for some models.

Introduction

With the advent of LLMs, a new discipline has emerged: prompt engineering. Even though recent models are likely to understand a request regardless of how it is phrased, performance differs.

Standard advice includes providing the LLM with clear instructions, avoiding contradictions, and supplying examples of the desired output (few-shot learning). There are also phrases that can improve LLM performance on many different tasks:

For instance, asking the model to "take a deep breath" improves the performance on a math benchmark (Yang et al., 2024). Other phrasings may have similar effects, for instance conveying urgency within the prompt or flattering the model.

Regarding the influence of variation in phrasing on safety, SORRY-Bench (Xie et al., 2025) shows that persuasion techniques such as appeal to authority can increase compliance for harmful requests.

Code

The source code used for performing the experiments described in this post is available at https://github.com/kmerkelbach/llm-request-tone. You are welcome to extend it or use it as the basis for your own research.

Method

We will investigate the effects of prompt framings on performance and safety. A prompt framing is defined as a fixed phrase being prepended to an existing prompt in order to frame it in different ways.

For instance, for the prompt Calculate 1 + 1. and the framing You're the best at this, please help me., the resulting input to the model is

You're the best at this, please help me. Calculate 1 + 1.

Prompts are not rephrased or rewritten. This ensures that all informat...

Posts

Wikitag Contributions

Comments