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cs.LG2025
Just Enough Shifts: Mitigating Over-Refusal in Aligned Language Models with Targeted Representation Fine-Tuning
Mahavir Dabas, Si Chen, Charles Fleming +2
Safety alignment is crucial for large language models (LLMs) to resist malicious instructions but often results in over-refusals, where benign prompts are unnecessarily rejected, i…
cs.AI2025
Strategize Globally, Adapt Locally: A Multi-Turn Red Teaming Agent with Dual-Level Learning
Si Chen, Xiao Yu, Ninareh Mehrabi +3
The exploitation of large language models (LLMs) for malicious purposes poses significant security risks as these models become more powerful and widespread. While most existing re…