A structured approach to assess LLM capabilities comprehensively, including defining evaluation objectives, selecting tasks and benchmarks, choosing metrics, designing an evaluation protocol, collecting and preparing data, executing the evaluation, analyzing results, iterating and refining, and considering key considerations such as bias and fairness.
Chain-of-Thought techniques are used to improve the reasoning abilities of large language models (LLMs) by encouraging them to think step-by-step before arriving at an answer.
