RST-Guarder: Enhancing Long-Context Robustness for Safeguards via RST Parsing and Probabilistic Inference

As large language models (LLMs) demonstrate remarkable capabilities across a wide range of tasks, ensuring the safety of their outputs is increasingly critical. To mitigate the risk of policy-violating responses, numerous guardrail models have been developed for harmful-content detection. While effective on short outputs, existing guardrails degrade on long-form responses, reflecting limited semantic understanding and weak robustness to contextual noise. To address these limitations, we propose RST-Guarder, an inference-time method that improves harmful-content detection for long-form inputs without additional data curation or model training. RST-Guarder first applies a RST parser to long-form inputs to get discourse-level semantic relations among segments, and subsequently performs hierarchical probabilistic inference to aggregate segment-level safety scores produced by pre-trained guardrail models. We evaluate RST-Guarder across multiple benchmarks and a diverse set of widely used guardrail models. Experimental results demonstrate that RST-Guarder consistently improves harmful-content detection on long-form inputs, while significantly reducing false positives that incorrectly classify benign content as harmful.