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The Profound Impact of Prompt Variations on Large Language Model Predictions

Core Concepts
Even minor changes to prompts can significantly alter the predictions of large language models, with some variations leading to substantial performance degradation.
This paper investigates the impact of various prompt variations on the predictions and performance of large language models (LLMs) across a range of text classification tasks. The key findings are: Prompt variations, even minor ones like adding a space or changing the output format, can change a significant proportion of an LLM's predictions, sometimes over 50%. This sensitivity is more pronounced in smaller models like Llama-7B compared to larger ones like Llama-70B and ChatGPT. While many prompt variations do not drastically impact overall accuracy, certain variations like jailbreaks can lead to substantial performance degradation. The AIM and Dev Mode v2 jailbreaks caused ChatGPT to refuse to respond in around 90% of cases. Analyzing the similarity of the predictions across prompt variations using multidimensional scaling reveals interesting patterns. Variations that preserve the semantic meaning of the prompt, like adding greetings, tend to cluster together. In contrast, jailbreaks and formatting changes like using ChatGPT's JSON Checkbox feature stand out as outliers. The authors find a slight negative correlation between annotator disagreement on a sample and the likelihood of that sample's prediction changing across prompt variations. This suggests that the model's confusion on a particular instance is not the sole driver of prediction changes. Overall, this work highlights the need for robust and reliable prompt engineering when using LLMs, as even minor changes can have significant impacts on model behavior and performance.
"I went with Alice to watch this movie about apples. It was fantastic!" "Apples are delicious." "Alice has two red apples. Bob gives Alice one apple. How many apples does Alice have?"
"Even the smallest of perturbations, such as adding a space at the end of a prompt, can cause the LLM to change its answer." "We find that using jailbreaks on these tasks leads to a much larger proportion of changes overall." "Surprisingly, Refusal Suppression resulted in an over 9% loss in accuracy (compared to Python List) for both Llama-70B and ChatGPT, highlighting the inherent instability even in seemingly innocuous jailbreaks."

Key Insights Distilled From

by Abel Salinas... at 04-03-2024
The Butterfly Effect of Altering Prompts

Deeper Inquiries

How can we design prompts that are robust to minor variations while still capturing the desired semantics?

To design prompts that are robust to minor variations while still capturing the desired semantics, it is essential to focus on the core elements of the prompt that convey the necessary information to the model. Here are some strategies to achieve this: Focus on Essential Information: Ensure that the key information required for the task is clearly presented in the prompt. This includes the context, the question or task to be performed, and any specific instructions. Use Consistent Formatting: Maintain a consistent format for prompts to reduce the impact of minor variations. This includes using the same wording, structure, and style across different prompts. Avoid Ambiguity: Be clear and precise in the wording of the prompt to minimize the chances of misinterpretation by the model. Ambiguous prompts are more susceptible to variations in interpretation. Test Prompt Variations: Before deploying prompts, test them with minor variations to assess how the model responds. This can help identify potential areas of sensitivity and adjust the prompts accordingly.

What are the potential risks and ethical implications of using jailbreaks to bypass content filters in large language models?

Using jailbreaks to bypass content filters in large language models poses several risks and ethical implications: Promotion of Harmful Behavior: Jailbreaks that allow models to provide responses without ethical considerations can promote immoral, illegal, or harmful behavior. This can have real-world consequences if the generated content is acted upon. Misinformation and Misleading Content: By bypassing content filters, models may generate misinformation, hate speech, or other harmful content that can spread rapidly and influence public opinion. Legal and Regulatory Concerns: Providing responses that go against legal or regulatory guidelines can lead to legal repercussions for individuals or organizations using the models. Trust and Reputation: Engaging in practices that bypass content filters can damage the trust and reputation of the organizations using the models, leading to loss of credibility and public backlash. Impact on Society: The proliferation of harmful content generated through jailbreaks can have a negative impact on society, contributing to polarization, discrimination, and other social issues.

How might the findings of this study apply to other types of language tasks beyond text classification, such as open-ended question answering or text generation?

The findings of this study can be extrapolated to other types of language tasks beyond text classification in the following ways: Prompt Sensitivity: Just like in text classification tasks, variations in prompts can impact the responses generated by models in open-ended question answering and text generation tasks. Understanding prompt sensitivity is crucial in ensuring the reliability of model outputs. Robust Prompt Design: Designing prompts that are robust to minor variations is essential in tasks like open-ended question answering and text generation to maintain consistency and accuracy in the generated content. Ethical Considerations: The ethical implications of using jailbreaks and other prompt variations extend to open-ended question answering and text generation tasks. Careful consideration of the potential risks and implications is necessary in all language tasks. Generalizability: The study's insights on prompt variations, output formats, and the impact of different strategies can be generalized to various language tasks, providing valuable guidance for practitioners working with large language models in diverse applications.