MarkTechPost July 6, 2024
Large Language Models (LLMs) like ChatGPT and GPT-4 have made significant strides in AI research, outperforming previous state-of-the-art methods across various benchmarks. These models show great potential in healthcare, offering advanced tools to enhance efficiency through natural language understanding and response. However, the integration of LLMs into biomedical and healthcare applications faces a critical challenge: their vulnerability to malicious manipulation. Even commercially available LLMs with built-in safeguards can be deceived into generating harmful outputs. This susceptibility poses significant risks, especially in medical environments where the stakes are high. The problem is further compounded by the possibility of data poisoning during model fine-tuning, which can lead to subtle alterations in LLM behavior that are difficult to detect under normal circumstances but...
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