Showing cs.LGShow all
2 papers · 1 filter
cs.LG2024
Maximizing the Potential of Synthetic Data: Insights from Random Matrix Theory
Aymane El Firdoussi, Mohamed El Amine Seddik, Soufiane Hayou +3
Synthetic data has gained attention for training large language models, but poor-quality data can harm performance (see, e.g., Shumailov et al. (2023); Seddik et al. (2024)). A pot…
cs.LG2024
Alignment with Preference Optimization Is All You Need for LLM Safety
Reda Alami, Ali Khalifa Almansoori, Ahmed Alzubaidi +3
We demonstrate that preference optimization methods can effectively enhance LLM safety. Applying various alignment techniques to the Falcon 11B model using safety datasets, we achi…