DINGO: Towards Diverse and Fine-Grained Instruction-Following Evaluation
Zihui Gu, Xingwu Sun, Fengzong Lian, Zhanhui Kang, Chengzhong Xu, Ju Fan
Abstract
Instruction-following is particularly crucial for large language models (LLMs) to support diverse user requests. While existing work has made progress in aligning LLMs with human preferences, evaluating their capabilities on instruction-following remains a challenge due to complexity and diversity of real-world user instructions. While existing evaluation methods focus on general skills, they suffer from two main shortcomings, i.e., lack of fine-grained task-level evaluation and reliance on singular instruction expression. To address these problems, this paper introduces DINGO, a fine-grained and diverse instruction-following evaluation dataset that has two main advantages: (1) DINGO is based on a manual annotated, fine-grained and multi-level category tree with 130 nodes derived from real-world user requests; (2) DINGO includes diverse instructions, generated by both GPT-4 and human experts. Through extensive experiments, we demonstrate that DINGO can not only provide more challenging and comprehensive evaluation for LLMs, but also provide task-level fine-grained directions to further improve LLMs.
BibTeX
@article{Gu_Sun_Lian_Kang_Xu_Fan_2024, title={DINGO: Towards Diverse and Fine-Grained Instruction-Following Evaluation}, volume={38}, url={https://ojs.aaai.org/index.php/AAAI/article/view/29768}, DOI={10.1609/aaai.v38i16.29768}, abstractNote={Instruction-following is particularly crucial for large language models (LLMs) to support diverse user requests. While existing work has made progress in aligning LLMs with human preferences, evaluating their capabilities on instruction-following remains a challenge due to complexity and diversity of real-world user instructions. While existing evaluation methods focus on general skills, they suffer from two main shortcomings, i.e., lack of fine-grained task-level evaluation and reliance on singular instruction expression. To address these problems, this paper introduces DINGO, a fine-grained and diverse instruction-following evaluation dataset that has two main advantages: (1) DINGO is based on a manual annotated, fine-grained and multi-level category tree with 130 nodes derived from real-world user requests; (2) DINGO includes diverse instructions, generated by both GPT-4 and human experts. Through extensive experiments, we demonstrate that DINGO can not only provide more challenging and comprehensive evaluation for LLMs, but also provide task-level fine-grained directions to further improve LLMs.}, number={16}, journal={Proceedings of the AAAI Conference on Artificial Intelligence}, author={Gu, Zihui and Sun, Xingwu and Lian, Fengzong and Kang, Zhanhui and Xu, Chengzhong and Fan, Ju}, year={2024}, month={Mar.}, pages={18108-18116} }