Title: Advancing LLM Safe Alignment with Safety Representation Ranking
Primary area: foundation or frontier models, including LLMs
Keywords: Alignment, Decoding, Transformer

Abstract: The rapid advancement of large language models (LLMs) has demonstrated milestone success in a variety of tasks, yet their potential for generating harmful content remains a significant safety concern. Existing safety guardrail approaches typically operate directly on textual responses, overlooking the rich information embedded in the model representations. In this paper, going beyond existing defenses that focus on a single safe response, we explore the potential of ranking hidden states across diverse responses to achieve safe generation. To this end, we propose Safety Representation Ranking (SRR), a listwise ranking framework that selects safe responses using hidden states from the LLM itself. SRR encodes both instructions and candidate completions using intermediate transformer representations and ranks candidates via a lightweight similarity-based scorer. Building on this framework, our approach directly leverages internal model states and supervision at the list level to capture subtle safety signals. Experiments across multiple benchmarks show that SRR significantly improves robustness to adversarial prompts, contributing a novel paradigm for LLM safety. Our code will be available upon publication.