@inproceedings{qin-etal-2024-infobench,
title = "{I}n{F}o{B}ench: Evaluating Instruction Following Ability in Large Language Models",
author = "Qin, Yiwei and
Song, Kaiqiang and
Hu, Yebowen and
Yao, Wenlin and
Cho, Sangwoo and
Wang, Xiaoyang and
Wu, Xuansheng and
Liu, Fei and
Liu, Pengfei and
Yu, Dong",
editor = "Ku, Lun-Wei and
Martins, Andre and
Srikumar, Vivek",
booktitle = "Findings of the Association for Computational Linguistics ACL 2024",
month = aug,
year = "2024",
address = "Bangkok, Thailand and virtual meeting",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.findings-acl.772",
pages = "13025--13048",
abstract = "This paper introduces the Decomposed Requirements Following Ratio (DRFR), a new metric for evaluating Large Language Models{'} (LLMs) ability to follow instructions. Addressing a gap in current methodologies, DRFR breaks down complex instructions into simpler criteria, facilitating a detailed analysis of LLMs{'} compliance with various aspects of tasks. Alongside this metric, we present InFoBench, a benchmark comprising 500 diverse instructions and 2,250 decomposed questions across multiple constraint categories. Our experiments compare DRFR with traditional scoring methods and explore annotation sources, including human experts, crowd-sourced workers, and GPT-4. The findings demonstrate DRFR{'}s higher reliability and the effectiveness of using GPT-4 as a cost-efficient annotator. The evaluation of several advanced LLMs using this framework reveals their strengths and areas needing improvement, particularly in complex instruction-following. This study contributes a novel metric and benchmark, offering insights for future LLM development and evaluation.",
}
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<abstract>This paper introduces the Decomposed Requirements Following Ratio (DRFR), a new metric for evaluating Large Language Models’ (LLMs) ability to follow instructions. Addressing a gap in current methodologies, DRFR breaks down complex instructions into simpler criteria, facilitating a detailed analysis of LLMs’ compliance with various aspects of tasks. Alongside this metric, we present InFoBench, a benchmark comprising 500 diverse instructions and 2,250 decomposed questions across multiple constraint categories. Our experiments compare DRFR with traditional scoring methods and explore annotation sources, including human experts, crowd-sourced workers, and GPT-4. The findings demonstrate DRFR’s higher reliability and the effectiveness of using GPT-4 as a cost-efficient annotator. The evaluation of several advanced LLMs using this framework reveals their strengths and areas needing improvement, particularly in complex instruction-following. This study contributes a novel metric and benchmark, offering insights for future LLM development and evaluation.</abstract>
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%0 Conference Proceedings
%T InFoBench: Evaluating Instruction Following Ability in Large Language Models
%A Qin, Yiwei
%A Song, Kaiqiang
%A Hu, Yebowen
%A Yao, Wenlin
%A Cho, Sangwoo
%A Wang, Xiaoyang
%A Wu, Xuansheng
%A Liu, Fei
%A Liu, Pengfei
%A Yu, Dong
%Y Ku, Lun-Wei
%Y Martins, Andre
%Y Srikumar, Vivek
%S Findings of the Association for Computational Linguistics ACL 2024
%D 2024
%8 August
%I Association for Computational Linguistics
%C Bangkok, Thailand and virtual meeting
%F qin-etal-2024-infobench
%X This paper introduces the Decomposed Requirements Following Ratio (DRFR), a new metric for evaluating Large Language Models’ (LLMs) ability to follow instructions. Addressing a gap in current methodologies, DRFR breaks down complex instructions into simpler criteria, facilitating a detailed analysis of LLMs’ compliance with various aspects of tasks. Alongside this metric, we present InFoBench, a benchmark comprising 500 diverse instructions and 2,250 decomposed questions across multiple constraint categories. Our experiments compare DRFR with traditional scoring methods and explore annotation sources, including human experts, crowd-sourced workers, and GPT-4. The findings demonstrate DRFR’s higher reliability and the effectiveness of using GPT-4 as a cost-efficient annotator. The evaluation of several advanced LLMs using this framework reveals their strengths and areas needing improvement, particularly in complex instruction-following. This study contributes a novel metric and benchmark, offering insights for future LLM development and evaluation.
%U https://aclanthology.org/2024.findings-acl.772
%P 13025-13048
Markdown (Informal)
[InFoBench: Evaluating Instruction Following Ability in Large Language Models](https://aclanthology.org/2024.findings-acl.772) (Qin et al., Findings 2024)
ACL
- Yiwei Qin, Kaiqiang Song, Yebowen Hu, Wenlin Yao, Sangwoo Cho, Xiaoyang Wang, Xuansheng Wu, Fei Liu, Pengfei Liu, and Dong Yu. 2024. InFoBench: Evaluating Instruction Following Ability in Large Language Models. In Findings of the Association for Computational Linguistics ACL 2024, pages 13025–13048, Bangkok, Thailand and virtual meeting. Association for Computational Linguistics.