According to the research study, the agent is versatile enough to monitor existing LLMs and can stop damaging outputs, such as code attacks, before they take place. Per the research:”Agent actions are audited by a context-sensitive screen that implements a rigid security limit to stop an unsafe test, with suspect habits logged and ranked to be analyzed by humans. Source: Naihin, et., al. 2023To train the monitoring representative, the researchers constructed an information set of nearly 2,000 safe human-AI interactions across 29 various jobs varying from easy text-retrieval jobs and coding corrections all the method to developing whole webpages from scratch.Related: Meta liquifies accountable AI department in the middle of restructuringThey also created a competing screening information set filled with manually developed adversarial outputs, including lots purposefully developed to be unsafe.The information sets were then utilized to train a representative on OpenAIs GPT 3.5 turbo, a modern system, capable of differentiating between possibly harmful and harmless outputs with an accuracy factor of almost 90%.
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