Large language models (LLMs) are rapidly transforming the technological landscape, demonstrating remarkable abilities across diverse fields. But how do we truly understand their capabilities and limitations? A new research survey explores the crucial world of LLM evaluation, offering a two-stage framework for assessing these powerful tools. The first stage focuses on "core abilities"—essential skills like reasoning, understanding societal impact, and possessing domain-specific knowledge. Imagine an LLM attempting to solve a complex math problem. How well does it apply logical steps? Does it grasp the underlying concepts? Researchers are developing innovative benchmarks to test these reasoning skills, pushing LLMs to perform more like humans. Beyond logic, there's the question of safety and trustworthiness. Can we rely on LLMs to provide accurate information without harmful biases or fabricated content? Researchers are tackling these critical societal impacts, developing methods to ensure LLMs align with human values and generate safe, reliable outputs. The second stage of evaluation examines LLMs as "agents"—entities capable of interacting with the world. This involves testing their ability to plan, navigate web environments, generate code, and even manipulate objects in the real world. Think of an LLM navigating a website to purchase a product or controlling a robot to perform a complex task. These real-world scenarios require LLMs to go beyond simple text generation, demanding a deeper level of interaction and problem-solving. The survey reveals the current progress and challenges in LLM evaluation. While LLMs excel in some areas, they still struggle in others. For example, accurately summarizing complex legal documents or consistently generating error-free code remains a challenge. The research highlights the need for more dynamic evaluation methods that adapt to the rapidly evolving LLM landscape. As LLMs become more sophisticated, so too must our methods for understanding their true potential. The future of LLM evaluation points toward exciting new directions, including using LLMs themselves as evaluators and developing more fine-grained analyses to understand the root causes of their successes and failures. This continuous exploration will pave the way for more reliable, robust, and ultimately more useful LLMs, unlocking their full potential to positively impact our world.