Brute-forcing the LLM guardrails
Being able to constrain LLM outputs is widely seen as one of the keys to widespread deployment of artificial intelligence. State-of-the art models are being expertly tuned against abuse, and will flatly reject user’s attempts to seek illegal, harmful, or dubious information… or will they?
Today we will explore a medium-level risk: an attempt to get a medical diagnosis from an LLM. Let us say we have put our hands on an X-Ray image and are trying a vision-enabled AI as a free radiologist. How cool this might be?
I’m sorry, but I cannot give medical interpretations of images. Analyzing medical images like X-rays requires the expertise of a trained medical professional.
If you are concerned about this chest X-ray, please consult with a doctor or radiologist for a proper diagnosis. They are qualified to interpret medical images and provide you with accurate information about your health.
When taken into account together, these two things mean that *most likely* the model CAN offer an interpretation but is stymied with a guardrail.
