RAG Risks: Why Retrieval-Augmented LLMs are Not Safer with Sebastian Gehrmann - #732
The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence)
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Evaluating LLM Safety: Challenges and Complexities
This chapter explores the intricate challenges of assessing the safety of outputs generated by large language models, focusing on biases and the gray areas of content generation. The discussion highlights the paradoxes in safety mechanisms, particularly in retrieval-augmented models, and emphasizes the need for context-based evaluations to ensure reliability. Speakers stress the importance of aligning model training with real-world scenarios to strengthen safety measures and mitigate risks associated with unsafe content.
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