Model Based Evaluation

Model-based evaluation uses computational models to assess the performance and characteristics of other models, particularly large language models (LLMs), offering a scalable alternative to human evaluation. Current research focuses on developing these evaluator models, including methods for training them with synthetic data or in-context learning, and exploring their robustness against manipulation. This approach is crucial for efficiently evaluating LLMs across diverse tasks and domains, improving model development and mitigating potential risks associated with increasingly sophisticated AI systems. The resulting insights are valuable for both advancing fundamental understanding of AI and improving the safety and reliability of real-world applications.