High-quality Data Do not Mean Safe! Poisoning LLMs after Data Selection
This research investigated whether selecting high-quality data removes poisoning samples from LLMs during fine-tuning. The study found that while selection removes overtly harmful examples, retaining high-quality samples can still degrade safety alignment because they possess training patterns similar to harmful data, causing safety conflicts to accumulate.