FollowEval: A Multi-Dimensional Benchmark for Assessing the
  Instruction-Following Capability of Large Language Models
Yimin Jing Renren Jin Jiahao Hu Huishi Qiu Xiaohua Wang Peng Wang Deyi Xiong
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.