Papers by Pratik Jalan
XGUARD: A Graded Benchmark for Evaluating Safety Failures of Large Language Models on Extremist Content (2026.findings-acl)
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| Challenge: | Existing safety evaluations rely on binary labels, overlooking the nuanced risk these outputs pose. |
| Approach: | They propose a framework to assess the severity of extremist content generated by Large Language Models (LLMs) it categorizes model responses into five danger levels (0–4) defined by degree of extremism endorsement . |
| Outcome: | The proposed framework categorizes model responses into five danger levels (0–4) defined by degree of extremist endorsement, enabling nuanced analysis of failure frequency and severity. |