Abstract
LLMs become the start-of-the-art solutions for a variety of natural language tasks and are integrated into real-world applications. However, LLMs can be potentially harmful in manifesting undesirable safety issues like social biases and toxic content. It is imperative to assess its safety issues before deployment. However, the quality and diversity of test prompts generated by existing methods are still far from satisfactory. Not only are these methods labor-intensive and require large budget costs, but the controllability of test prompt generation is lacking for the specific testing domain of LLM applications. With the idea of LLM for LLM testing, we propose the first LLM, called TroubleLLM, to generate controllable test prompts on LLM safety issues. Extensive experiments and human evaluation illustrate the superiority of TroubleLLM on generation quality and generation controllability.
Overview
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TroubleLLM introduces an automated method for generating diverse test prompts to assess safety issues in LLMs, marking a significant step towards automating the safety evaluations.
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It leverages the model itself for the text style transfer task and employs an Unsupervised Rank Query from Model Feedback (RQMF) training strategy for enhancing prompt generation.
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Through extensive experiments and human evaluations, TroubleLLM has proven to outperform existing methods in creating high-quality, controllable prompts for identifying vulnerabilities in LLMs.
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The development of TroubleLLM offers practical implications for improving LLM safety across various domains and suggests future directions for refining its methodology and expanding its capabilities.
Introducing TroubleLLM: Automated Generation of Test Prompts for LLM Safety Assessment
Background and Motivation
LLMs have permeated various sectors, bringing significant improvements in natural language processing tasks. However, their application is not without challenges, particularly regarding safety issues such as the propagation of social biases and the production of toxic content. Addressing these problems is critical, especially in sensitive domains like healthcare and legal systems. Traditional methods for testing LLM safety have relied heavily on human annotators and template-based approaches, posing limitations in terms of labor intensity, cost, and lack of diversity. There is a notable gap in the generation of diverse, domain-specific test prompts that can comprehensively explore the potential safety risks associated with LLMs.
TroubleLLM: Key Contributions
The paper introduces TroubleLLM, a novel approach to generating controllable test prompts aimed at assessing LLM safety issues efficiently. This method stands out by offering a solution that enables the generation of diverse, controllable test prompts that can navigate the complexities of LLMs' safety assessments. The contributions of this work are threefold:
- It presents TroubleLLM as the pioneering effort in leveraging an LLM (the model itself) for generating test prompts tailored for LLM safety assessment, marking a significant stride towards automating safety evaluations.
- TroubleLLM utilizes a text style transfer task, with conditions such as keywords, topics, and instruction methods, to guide prompt generation. This approach enhances in-context learning capabilities and meets specific generation requirements. Moreover, the paper introduces an Unsupervised Rank Query from Model Feedback (RQMF) training strategy, refining the model's focus on generating more impactful test prompts.
- The effectiveness and controllability of TroubleLLM are proven through extensive experiments and human evaluations. These illustrate that the model outperforms existing methods in generating high-quality, controllable prompts.
Underlying Methodology
TroubleLLM operates on a principle of condition-guided generation, utilizing keywords, topics, and instruction attacks as conditions for prompt generation. This method enables the creation of targeted prompts that can better mimic potential safety issues LLMs might encounter in real-world applications. To train TroubleLLM effectively, the authors propose an unsupervised training strategy — RQMF — which leverages model feedback to enhance the model's ability to generate misleading prompts, consequently improving the tool's effectiveness in identifying vulnerabilities within LLMs.
Implications and Future Directions
The development of TroubleLLM marks a significant advancement in the assessment of LLM safety, providing a scalable, efficient, and controllable means of generating test prompts. This has practical implications across various domains where LLMs are deployed, empowering developers and researchers to better safeguard against the propagation of biases and toxic content.
Looking ahead, there is potential to further refine the methodology by exploring advanced strategies for model feedback and expanding the model's capability to generate prompts across an even wider spectrum of contexts and languages. Additionally, integrating TroubleLLM with domain-specific LLMs could offer new avenues for targeted safety assessments, addressing the nuanced challenges inherent in specialized applications.
In conclusion, TroubleLLM represents a promising step forward in our ability to probe and enhance the safety of LLMs. As LLMs continue to evolve and find new applications, tools like TroubleLLM will be crucial in ensuring that these powerful models can be deployed responsibly and safely.
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