Trustworthy LLMs: A Survey and Guideline for Evaluating Large Language Models’ Alignment

In this paper review, we discussed how to create a golden dataset for evaluating LLMs using evals from alignment tasks. The process involves running eval tasks, gathering examples, and fine-tuning or prompt engineering based on the results. We also touched upon the use of RAG systems in AI observability and the importance of evals in improving model performance.