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Article
Exploring the Growing Challenge of Large Language Model Validation
How do you validate a system that mimics human reasoning? Large Language Models introduce complexities that defy traditional validation methods. This article explores the challenges, ethical considerations, and evolving industry standards for ensuring these AI systems are accurate, reliable, and aligned with human values.
Nov 25, 2024
Indra Reddy Mallela, VP - Model Risk Manager, MUFG Bank
Tags:
Model risk
AI and Technology (including Fintech)
The views and opinions expressed in this content are those of the thought leader as an individual and are not attributed to CeFPro or any other organization
- Validating Large Language Models requires context-aware assessments due to their use of unstructured data and human-like outputs.
- Challenges like data quality, interpretability, and "hallucinations" highlight the need for rigorous and innovative validation methods.
- Emerging performance metrics such as HELM, GLUE, and MMLU are shaping industry standards for evaluating AI capabilities.
- Ethical considerations, including bias, transparency, and data privacy, remain critical in the responsible validation and deployment of LLMs.
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