  Recently, the evaluation of Large Language Models has emerged as a popular
area of research. The three crucial questions for LLM evaluation are ``what,
where, and how to evaluate''. However, the existing research mainly focuses on
the first two questions, which are basically what tasks to give the LLM during
testing and what kind of knowledge it should deal with. As for the third
question, which is about what standards to use, the types of evaluators, how to
score, and how to rank, there hasn't been much discussion. In this paper, we
analyze evaluation methods by comparing various criteria with both manual and
automatic evaluation, utilizing onsite, crowd-sourcing, public annotators and
GPT-4, with different scoring methods and ranking systems. We propose a new
dataset, LLMEval and conduct evaluations on 20 LLMs. A total of 2,186
individuals participated, leading to the generation of 243,337 manual
annotations and 57,511 automatic evaluation results. We perform comparisons and
analyses of different settings and conduct 10 conclusions that can provide some
insights for evaluating LLM in the future. The dataset and the results are
publicly available at https://github.com/llmeval .
