reviews: 
Comments: This paper introduces HarmAug, a method for creating efficient safety guard models for LLMs. They propose to distill large safety models into smaller ones where the output is binary label indicating whether the input is safe or not. To overcome the limited diversity of harmful instructions in existing datasets, they introduce HarmAug, a data augmentation method that involves:
   a. Prompting an LLM to generate harmful instructions
   b. Using an affirmative prefix to encourage the LLM to complete the harmful instruction
   c. Generating responses to these instructions using another LLM
   d. Labeling the instruction-response pairs using the teacher model

Based on this they are able to outperform much large LLMs (7B compare to ~500Mb), showing the importance of synthetic data generation and knowledge distillation to create small robust safety guardrail classifiers.
Strengths: Originality:
1. The main novelty is HarmAug, the data augmentation method for generating harmful instructions to train safety guard models.
2. They also propose an effective "prefix attack" to bypass safety guardrails of language models when generating harmful content for training data.
3. Results show the feasibility of distilling large safety guard models into much smaller, more efficient models without significant performance loss.

Quality:
1. The results section is mostlycomprehensive in the experiments across multiple benchmark datasets.
2. Provides detailed ablation studies to analyze the impact of different components and design choices.
3. Compares the proposed method against several relevant baselines and existing safety guard models.
4. Evaluates both performance (F1 score, AUPRC) and efficiency metrics (FLOPs, latency, memory usage).

Clarity:
1. Clearly explains the motivation, methodology, and experimental setup.
2. Uses effective visualizations to illustrate key results and comparisons.
3. Provides a detailed breakdown of the proposed method, making it easier for others to reproduce or build upon.

Significance:
1. Addresses an important practical challenge in deploying safety guard models on resource-constrained devices.
2. Demonstrates that smaller models can achieve comparable or better performance than much larger models for this task.
3. Shows potential for improving the efficiency of red-teaming and fine-tuning processes for language model safety.
4. Provides open-source code, models, and datasets to facilitate further research in this area.
Weaknesses: * Limited Analysis of Jailbreaking Technique: While the prefix attack is effective, the paper doesn't provide an in-depth analysis of why this particular method works or explore alternative jailbreaking techniques. 
* Generally, if the novelty lies in HarmAug in diversifying generated data it would be nice to see comparison to other instructions and their failure modes w.r.t to HarmAug. 
* Table 4 could also include other prefixes as baselines to further motivate that particular string of texts effectiveness. 
* the utility of these models on mobile devices is mentioned a few times but never experimented with on-device. Arguably outside of the paper but would definitely be a stronger set of results to have.
Recommendation: 6
Reviewer confidence: 4
Soundness/correctness: 4
Clarity: 4