Unresolved Issues: Summarizing Contradictory Knowledge and Robust Safety Alignment

Develop techniques that enable large language models trained via probabilistic modeling to summarize contradictory training knowledge into coherent outputs and to maintain robust safety alignment that resists jailbreak attacks.

Background

The authors note that probabilistic language modeling lacks mechanisms to select and synthesize conflicting information, leading to unstable or biased outputs. They also highlight that current safety alignment remains vulnerable to jailbreaks, indicating unsolved robustness issues even after alignment and data cleaning.