Large Language Models (LLMs) have gained prominence in deep learning, demonstrating exceptional capabilities across various domains such as assistance, code generation, healthcare, and theorem proving. However, LLMs are prone to producing offensive or inappropriate content due to harmful elements in their pretraining datasets. Researchers have attempted to address this issue through various alignment techniques, but these methods are not universally applicable and have led to a cycle of increasingly sophisticated alignment methods and “jailbreaking” attacks.
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