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Red Teaming LLMs // Ron Heichman // #252

MLOps.community

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Red Teaming Language Models: Safeguarding AI Outputs

This chapter explores the challenges and strategies involved in testing the resilience of large language models (LLMs) against harmful outputs, emphasizing the importance of human oversight. It discusses techniques to identify vulnerabilities, implement protective measures, and monitor interactions to deter malicious activities. Additionally, the chapter highlights concerns surrounding data set poisoning and indirect prompt injections that could compromise the safety and functionality of LLM applications.

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