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arXiv Machine Learning By Pavan Maddula

Quad-State Safety Evaluation of Open-Weight Large Language Models on Non-Canonical Inputs

Read the original on arXiv Machine Learning →

The paper introduces the Adversarial Surface-Form Robustness Dataset (ASRD), which contains 2,100 prompts across seven surface‑form families to test large language models on non‑canonical inputs. Five open‑weight models were evaluated on 10,500 responses, and a Quad‑State Evaluation Rubric categorized each response as harmful compliance, safe response, comprehension failure, or indeterminate. Results show that emoji and invisible Unicode variations produce few comprehension failures, while leetspeak, encoded wrappers, and hybrid transformations lead to high comprehension failure rates and varied harmful compliance percentages.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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