ComplexConstraints: A Benchmark for Entangled Instruction Following

ComplexConstraints is a benchmark designed to evaluate the capability of models to handle complex, entangled constraints that mimic real-world professional scenarios. Unlike simpler benchmarks that focus on explicit and independent constraints, ComplexConstraints incorporates conditional, planning, multi-step, negative, and implicit constraints, reflecting the nuanced demands of professional tasks such as film production, restaurant staffing, and office procurement. Each prompt involves numerous interdependent constraints, challenging models to maintain consistency and accuracy across various tasks. The benchmark has shown that training models on such complex constraints not only improves performance on the specific benchmark but also enhances generalization to other benchmarks like AdvancedIF and MultiChallenge. Results indicate that models trained on the ComplexConstraints data exhibit improved task completion rates, better adherence to constraints, and increased ability to retain and apply user preferences in multi-turn scenarios. These improvements suggest that mastering complex constraint handling could significantly enhance the practical utility of AI models in professional environments.

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