← 研踪 PaperTrailACL Anthology · ACL 2026:acl:2026.acl-long.1025 · 记录 #1
RST-Guarder: Enhancing Long-Context Robustness for Safeguards via RST Parsing and Probabilistic Inference
来源日期（仅到月份） 2026-07
来源记录日期 2026-07
雷达观测 2026-11-01 21:56
摘要
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.
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记录 #1 · 2026-07 · 最新观测版本
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.
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