The effective assessment of the instruction-following ability of large language models (LLMs) is of paramount importance. A model that cannot adhere to human instructions might be not able to provide reliable and helpful responses. In pursuit of this goal, various benchmarks have been constructed to evaluate the instruction-following capacity of these models. However, these benchmarks are limited to a single language and are constructed using automated approaches, which restricts their applicability and the quality of the test examples they contain. To bridge this gap, we introduce the FollowEval benchmark in this paper. This benchmark is composed of instances in both English and Chinese, and all test examples are crafted by human experts. Furthermore, the FollowEval benchmark is designed to assess LLMs across five critical dimensions of instruction following: string manipulation, commonsense reasoning, logical reasoning, spatial reasoning, and response constraints. To enhance the complexity and present a sufficient challenge, each test example is designed to evaluate more than one dimension. We have evaluated various LLMs using the FollowEval benchmark and found that their performance significantly lags behind that of humans. This highlights the considerable room for improvement in the instruction-following ability of these models.

这篇论文介绍了FollowEval基准测试，通过人工专家设计的测试实例来评估大型语言模型在指令跟随能力方面的表现，测试涵盖了字符串处理、常识推理、逻辑推理、空间推理和响应约束等五个关键维度，并发现这些模型在指令跟随能力方面明显落后于人类，指出了这些模型在这方面还有很大的改进空间。

FollowEval: 评估大型语言模型指令遵循能力的多维度基准