The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence) Ensuring LLM Safety for Production Applications with Shreya Rajpal - #647
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Sep 18, 2023 Shreya Rajpal, Founder and CEO of Guardrails AI, dives deep into the critical topic of ensuring safety and reliability in language models for production use. She discusses the various risks associated with LLMs, especially the challenges of hallucinations and their implications. The conversation navigates the need for robust evaluation metrics and innovative tools like Guardrails, an open-source project designed to enforce model correctness. Shreya also highlights the importance of validation systems and their role in enhancing the safety of NLP applications.
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LLM Challenges
- LLMs hallucinate; they confidently produce incorrect outputs.
- Besides hallucinations, LLMs struggle with domain-specific constraints, like avoiding mentioning competitors.
LLM Risk Taxonomy
- Hallucinations are a subset of LLM risks, including performance, brand, and compliance risks.
- Domain-specific constraints, like a medical chatbot avoiding medical advice, pose a substantial challenge.
Hallucination Types
- Closed-domain hallucination occurs in RAG when LLMs inject information not found in provided documents.
- Open-domain hallucination happens when LLMs draw upon their broader training data when answering.