Instruction-Following Evaluation for Large Language Models
Jeffrey Zhou Tianjian Lu Swaroop Mishra Siddhartha Brahma Sujoy Basu Yi Luan Denny Zhou Le Hou
One core capability of Large Language Models (LLMs) is to follow natural
language instructions. However, the evaluation of such abilities is not
standardized: Human evaluations are expensive, slow, and not objectively
reproducible, while LLM-based auto-evaluation is potentially biased or limited
by the ability of the evaluator LLM. To overcome these issues, we introduce
Instruction-Following Eval (IFEval) for large language models. IFEval is a
straightforward and easy-to-reproduce evaluation benchmark. It focuses on a set
of "verifiable instructions" such as "write in more than 400 words" and
"mention the keyword of AI at least 3 times". We identified 25 types of those
verifiable instructions and constructed around 500 prompts, with each prompt
containing one or more verifiable instructions. We show evaluation results of
two widely available LLMs on the market. Our code and data can be found at
https://github.com/google-research/google-research/tree/master/instruction_following_eval