Benchmarking Large Language Models on Controllable Generation under
  Diversified Instructions
Yihan Chen Benfeng Xu Quan Wang Yi Liu Zhendong Mao
While large language models (LLMs) have exhibited impressive
instruction-following capabilities, it is still unclear whether and to what
extent they can respond to explicit constraints that might be entailed in
various instructions. As a significant aspect of LLM alignment, it is thus
important to formulate such a specialized set of instructions as well as
investigate the resulting behavior of LLMs. To address this vacancy, we propose
a new benchmark CoDI-Eval to systematically and comprehensively evaluate LLMs'
responses to instructions with various constraints. We construct a large
collection of constraints-attributed instructions as a test suite focused on
both generalization and coverage. Specifically, we advocate an instruction
diversification process to synthesize diverse forms of constraint expression
and also deliberate the candidate task taxonomy with even finer-grained
sub-categories. Finally, we automate the entire evaluation process to
facilitate further developments. Different from existing studies on
controllable text generation, CoDI-Eval extends the scope to the prevalent
instruction-following paradigm for the first time. We provide extensive
evaluations of representative LLMs (e.g., ChatGPT, Vicuna) on CoDI-Eval,
revealing their limitations in following instructions with specific constraints
and there is still a significant gap between open-source and commercial
closed-source LLMs. We believe this benchmark will facilitate research into
improving the controllability of LLMs' responses to instructions. Our data and
code are available at https://github.com/Xt-cyh/CoDI-Eval.