Increased LLM Vulnerabilities from Fine-tuning and Quantization: Conclusion and References | HackerNoon
Briefly

from Hackernoon4 months ago
The investigation reveals how fine-tuned and quantized LLMs are particularly vulnerable to jailbreaking attempts, emphasizing the essential nature of implementing rigorous guardrails and protocols.
Hackernoonhttps://hackernoon.com/increased-llm-vulnerabilities-from-fine-tuning-and-quantization-conclusion-and-references
Fine-tuning can lead to catastrophic forgetting, where the model loses safety protocols, or shifts focus to new topics, suggesting that safety alignment must be continuously monitored.
Hackernoonhttps://hackernoon.com/increased-llm-vulnerabilities-from-fine-tuning-and-quantization-conclusion-and-references
Our proposed solution involves integrating comprehensive safety measures during the fine-tuning process and implementing CI/CD stress tests to ensure robust defenses against jailbreak attempts.
Hackernoonhttps://hackernoon.com/increased-llm-vulnerabilities-from-fine-tuning-and-quantization-conclusion-and-references
Promoting ethical AI deployment relies on both innovative advancements in AI technology and stringent safety practices, establishing new standards for responsible AI development to guard against misuse.
Hackernoonhttps://hackernoon.com/increased-llm-vulnerabilities-from-fine-tuning-and-quantization-conclusion-and-references
Read at Hackernoon
#ai-safety #jailbreaking #llm-vulnerability #ethical-ai #model-fine-tuning
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