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🧠 AI🟢 BullishImportance 7/10
Mitigating Over-Refusal in Aligned Large Language Models via Inference-Time Activation Energy
arXiv – CS AI|Eric Hanchen Jiang, Weixuan Ou, Run Liu, Shengyuan Pang, Guancheng Wan, Ranjie Duan, Wei Dong, Kai-Wei Chang, XiaoFeng Wang, Ying Nian Wu, Xinfeng Li||3 views
🤖AI Summary
Researchers introduce Energy Landscape Steering (ELS), a new framework that reduces false refusals in AI safety-aligned language models without compromising security. The method uses an external Energy-Based Model to dynamically guide model behavior during inference, improving compliance from 57.3% to 82.6% on safety benchmarks.
Key Takeaways
- →ELS addresses the over-refusal problem where safety-aligned AI models incorrectly reject benign requests.
- →The framework uses a lightweight external Energy-Based Model to steer AI behavior in real-time without modifying core parameters.
- →Testing showed compliance improvements from 57.3% to 82.6% on the ORB-H benchmark while maintaining safety standards.
- →The approach is computationally efficient and fine-tuning free, making it practical for deployment.
- →ELS decouples behavioral control from the model's core knowledge, providing a flexible safety solution.
#ai-safety#language-models#machine-learning#energy-based-models#inference-optimization#ai-alignment#research#arxiv
Read Original →via arXiv – CS AI
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