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    <abstract>With the development and widespread application of large language models (LLMs), the new paradigm of “Model as Product” is rapidly evolving, and demands higher capabilities to address complex user needs, often requiring precise workflow execution which involves the accurate understanding of multiple tasks. However, existing benchmarks focusing on single-task environments with limited constraints, lack the complexity required to fully reflect To bridge this gap, we present the Extremely Complex Instruction Following Benchmark (EIFBENCH), meticulously crafted to facilitate a more realistic and robust evaluation of LLMs. EIFBENCH not only includes multi-task scenarios that enable comprehensive assessment across diverse task types concurrently, but also integrates a variety of constraints, replicating complex operational environments. Furthermore, we propose the Segment Policy Optimization (SegPO) algorithm to enhance the LLM’s ability to accurately fulfill multi-task workflow. Evaluations on EIFBENCH have unveiled considerable performance discrepancies in existing LLMs when challenged with these extremely complex instructions. This finding underscores the necessity for ongoing optimization to navigate the intricate challenges posed by real-world LLM applications.</abstract>
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%0 Conference Proceedings
%T EIFBENCH: Extremely Complex Instruction Following Benchmark for Large Language Models
%A Zou, Tao
%A Zhang, Xinghua
%A Yu, Haiyang
%A Wang, Minzheng
%A Huang, Fei
%A Li, Yongbin
%Y Christodoulopoulos, Christos
%Y Chakraborty, Tanmoy
%Y Rose, Carolyn
%Y Peng, Violet
%S Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-332-6
%F zou-etal-2025-eifbench
%X With the development and widespread application of large language models (LLMs), the new paradigm of “Model as Product” is rapidly evolving, and demands higher capabilities to address complex user needs, often requiring precise workflow execution which involves the accurate understanding of multiple tasks. However, existing benchmarks focusing on single-task environments with limited constraints, lack the complexity required to fully reflect To bridge this gap, we present the Extremely Complex Instruction Following Benchmark (EIFBENCH), meticulously crafted to facilitate a more realistic and robust evaluation of LLMs. EIFBENCH not only includes multi-task scenarios that enable comprehensive assessment across diverse task types concurrently, but also integrates a variety of constraints, replicating complex operational environments. Furthermore, we propose the Segment Policy Optimization (SegPO) algorithm to enhance the LLM’s ability to accurately fulfill multi-task workflow. Evaluations on EIFBENCH have unveiled considerable performance discrepancies in existing LLMs when challenged with these extremely complex instructions. This finding underscores the necessity for ongoing optimization to navigate the intricate challenges posed by real-world LLM applications.
%U https://aclanthology.org/2025.emnlp-main.1059/
%P 20941-20964

Markdown (Informal)

[EIFBENCH: Extremely Complex Instruction Following Benchmark for Large Language Models](https://aclanthology.org/2025.emnlp-main.1059/) (Zou et al., EMNLP 2025)

• EIFBENCH: Extremely Complex Instruction Following Benchmark for Large Language Models (Zou et al., EMNLP 2025)

ACL

• Tao Zou, Xinghua Zhang, Haiyang Yu, Minzheng Wang, Fei Huang, and Yongbin Li. 2025. EIFBENCH: Extremely Complex Instruction Following Benchmark for Large Language Models. In Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing, pages 20941–20964, Suzhou, China. Association for Computational Linguistics.