  Large language models (LLMs) have shown the potential to be integrated into
human daily lives. Therefore, user preference is the most critical criterion
for assessing LLMs' performance in real-world scenarios. However, existing
benchmarks mainly focus on measuring models' accuracy using multi-choice
questions, which limits the understanding of their capabilities in real
applications. We fill this gap by proposing a comprehensive Chinese benchmark
SuperCLUE, named after another popular Chinese LLM benchmark CLUE. SuperCLUE
encompasses three sub-tasks: actual users' queries and ratings derived from an
LLM battle platform (CArena), open-ended questions with single and
multiple-turn dialogues (OPEN), and closed-ended questions with the same stems
as open-ended single-turn ones (CLOSE). Our study shows that accuracy on
closed-ended questions is insufficient to reflect human preferences achieved on
open-ended ones. At the same time, they can complement each other to predict
actual user preferences. We also demonstrate that GPT-4 is a reliable judge to
automatically evaluate human preferences on open-ended questions in a Chinese
context. Our benchmark will be released at https://www.CLUEbenchmarks.com
