CCR-Bench: A Comprehensive Benchmark for Evaluating LLMs on Complex Constraints, Control Flows, and Real-World Cases

TL;DR

CCR-Bench is a new benchmark that rigorously evaluates large language models on their ability to understand and execute complex, real-world instructions involving intricate constraints, control flows, and task decompositions.

Contribution

This paper introduces CCR-Bench, a comprehensive and realistic benchmark that captures the high-dimensional complexity of real-world instructions for evaluating LLMs.

Findings

State-of-the-art models show significant performance gaps on CCR-Bench.

CCR-Bench reveals the limitations of current LLMs in handling complex instructions.

Real-world industrial scenarios are effectively represented in the benchmark.

Abstract

Enhancing the ability of large language models (LLMs) to follow complex instructions is critical for their deployment in real-world applications. However, existing evaluation methods often oversimplify instruction complexity as a mere additive combination of atomic constraints, failing to adequately capture the high-dimensional complexity arising from the intricate interplay of content and format, logical workflow control, and real-world applications. This leads to a significant gap between current evaluation practices and practical demands. To bridge this gap, we introduce CCR-Bench, a novel benchmark designed to assess LLMs' adherence to complex instructions. CCR-Bench is characterized by: (1) deep entanglement of content and formatting requirements in task specifications; (2) instructions that involve intricate task decomposition, conditional reasoning, and procedural planning; and (3) evaluation samples derived entirely from real-world industrial scenarios. Extensive experiments on CCR-Bench demonstrate that even state-of-the-art models exhibit substantial performance deficiencies, clearly quantifying the gap between current LLM capabilities and the demands of realworld instruction understanding. We believe that CCR-Bench offers a more rigorous and realistic evaluation framework, advancing the development of LLMs toward the next generation of models capable of understanding and executing complex tasks in industrial applications.