This paper presents FoFo, a pioneering benchmark for evaluating large language models' (LLMs) ability to follow complex, domain-specific formats, a crucial yet underexamined capability for their application as AI agents. Despite LLMs' advancements, existing benchmarks fail to assess their format-following proficiency adequately. FoFo fills this gap with a diverse range of real-world formats and instructions, developed through an AI-Human collaborative method. Our evaluation across both open-source (e.g., Llama 2, WizardLM) and closed-source (e.g., GPT-4, PALM2, Gemini) LLMs highlights three key findings: open-source models significantly lag behind closed-source ones in format adherence; LLMs' format-following performance is independent of their content generation quality; and LLMs' format proficiency varies across different domains. These insights suggest the need for specialized tuning for format-following skills and highlight FoFo's role in guiding the selection of domain-specific AI agents. FoFo is released here at https://github.com/SalesforceAIResearch/FoFo.
@article{arxiv.2402.18667,
title = {FOFO: A Benchmark to Evaluate LLMs' Format-Following Capability},
author = {Congying Xia and Chen Xing and Jiangshu Du and Xinyi Yang and Yihao Feng and Ran Xu and Wenpeng Yin and Caiming Xiong},
journal= {arXiv preprint arXiv:2402.18667},
year = {2024}
}