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Rom Himelstein

2 accepted papers

2026

Silenced Biases: The Dark Side LLMs Learned to Refuse

AAAI 2026technical

Safety-aligned large language models (LLMs) are becoming increasingly widespread, especially in sensitive applications where fairness is essential and biased outputs can cause significant harm. However, evaluating the fairness of models is a complex challenge, and approaches that do so typically uti

Cited by 0SourcePDFScholar
2025

Jailbreak Attack Initializations as Extractors of Compliance Directions

EMNLP 2025

Safety-aligned LLMs respond to prompts with either compliance or refusal, each corresponding to distinct directions in the model’s activation space. Recent studies have shown that initializing attacks via self-transfer from other prompts significantly enhances their performance. However, the underly

Cited by 0SourcePDFScholar