arXiv:2606.00160v1 Announce Type: cross Abstract: Large language models (LLMs) suffer from degraded safety capabilities even when fine-tuned with benign datasets. However, existing methods for identifying safety-degrading samples in benign datasets suffer from high computational costs and significant noise issues. In this paper, we propose DataShield to efficiently and effectively identify potential safety-degrading samples. Our key intuition is based on the observation that benign fine-tuning increases the overall response compliance of LLMs. DataShield's key technical insight is to quantify
As LLMs become more integrated into critical applications, ensuring their safety and preventing 'safety-degrading' behavior from training data is a pressing and immediate concern for deployment.
The ability to efficiently filter training data for safety-degrading samples is crucial for the reliable and ethical development and deployment of LLMs, especially as their capabilities expand.
This research introduces a more efficient method for identifying problematic data, potentially accelerating the development of safer and more robust LLMs without incurring prohibitive computational costs.
- · LLM developers
- · AI safety researchers
- · Enterprises deploying LLMs
- · Malicious actors exploiting LLM vulnerabilities
- · Inefficient AI data curation methods
- · LLM projects relying solely on unvetted public datasets
LLMs can be fine-tuned with greater confidence in their safety, leading to wider adoption in sensitive domains.
Improved safety filtering methods could accelerate competition among LLM providers based on ethical deployment and reliability metrics.
Reduced risk of safety failures might broaden regulatory acceptance and public trust in advanced AI applications.
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Read at arXiv cs.CL