A Lightweight Explainable Guardrail for Prompt Safety
arXiv:2602.15853v1 Announce Type: cross Abstract: We propose a lightweight explainable guardrail (LEG) method for the classification of unsafe prompts. LEG uses a multi-task learning architecture to jointly learn a prompt classifier and an explanation classifier, where the latter labels prompt words that explain the safe/unsafe overall decision. LEG is trained using synthetic data for explainability, which is generated using a novel strategy that counteracts the confirmation biases of LLMs. Lastly, LEG's training process uses a novel loss that captures global explanation signals and combines cross-entropy and focal losses with uncertainty-based weighting. LEG obtains equivalent or better performance than the state-of-the-art for both prompt classification and explainability, both in-domain and out-of-domain on three datasets, despite the fact that its model size is considerably smaller than current approaches. If accepted, we will release all models and the annotated dataset publicly.
arXiv:2602.15853v1 Announce Type: cross Abstract: We propose a lightweight explainable guardrail (LEG) method for the classification of unsafe prompts. LEG uses a multi-task learning architecture to jointly learn a prompt classifier and an explanation classifier, where the latter labels prompt words that explain the safe/unsafe overall decision. LEG is trained using synthetic data for explainability, which is generated using a novel strategy that counteracts the confirmation biases of LLMs. Lastly, LEG's training process uses a novel loss that captures global explanation signals and combines cross-entropy and focal losses with uncertainty-based weighting. LEG obtains equivalent or better performance than the state-of-the-art for both prompt classification and explainability, both in-domain and out-of-domain on three datasets, despite the fact that its model size is considerably smaller than current approaches. If accepted, we will release all models and the annotated dataset publicly.
Executive Summary
The article proposes a lightweight explainable guardrail (LEG) method for classifying unsafe prompts in large language models. LEG utilizes a multi-task learning architecture to jointly learn a prompt classifier and an explanation classifier, which labels words explaining the safe/unsafe decision. The method is trained using synthetic data generated to counteract confirmation biases in LLMs and employs a novel loss function that captures global explanation signals. LEG achieves equivalent or better performance than state-of-the-art models on three datasets, despite having a significantly smaller model size. This breakthrough has the potential to improve the transparency and accountability of AI decision-making processes.
Key Points
- ▸ LEG proposes a multi-task learning architecture for prompt classification and explanation generation
- ▸ The method utilizes synthetic data generated to counteract confirmation biases in LLMs
- ▸ LEG achieves equivalent or better performance than state-of-the-art models on three datasets
Merits
Advancements in Explainability
LEG's ability to generate explanations for prompt classification decisions enhances the transparency and accountability of AI decision-making processes.
Improved Performance
LEG achieves equivalent or better performance than state-of-the-art models on three datasets, despite having a significantly smaller model size.
Efficient Training Process
LEG's training process uses a novel loss function that captures global explanation signals, enabling efficient training and reduced computational resources.
Demerits
Limited Dataset
The study relies on three datasets, which may not be representative of the broader range of potential applications and scenarios.
Potential Bias in Synthetic Data
The use of synthetic data generated to counteract confirmation biases in LLMs may introduce new biases or artifacts in the training process.
Expert Commentary
The article presents a significant advancement in the field of explainable AI, leveraging multi-task learning and synthetic data to develop a lightweight and efficient method for prompt classification and explanation generation. While the study's results are promising, the limitations of the approach and the potential for bias in synthetic data are concerns that warrant further investigation. The development of LEG has the potential to improve the transparency and accountability of AI decision-making processes, and its implications for policy and practice are substantial. Nevertheless, the study's focus on a specific application and dataset may limit its generalizability, and further research is needed to explore the broader applicability of LEG.
Recommendations
- ✓ Future research should investigate the robustness and transferability of LEG across different applications and datasets.
- ✓ The development of more comprehensive and diverse datasets is essential for ensuring the generalizability and reliability of LEG.
