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

Xiaona Xue, Yiqiao Huang, Jiacheng Li, Yuanhang Zheng, Huiqi Miao, Yunfei Ma, Rui Liu, Xinbao Sun, Minglu Liu, Fanyu Meng, Chao Deng, Junlan Feng

TL;DR

CCR-Bench is introduced, a novel benchmark designed to assess LLMs' adherence to complex instructions, and believes 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.

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.

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

TL;DR

CCR-Bench is introduced, a novel benchmark designed to assess LLMs' adherence to complex instructions, and believes 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.

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.
Paper Structure (47 sections, 1 equation, 19 figures, 12 tables)

This paper contains 47 sections, 1 equation, 19 figures, 12 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Instruction Following
  4. Evaluation Methods
  5. Human Evaluation
  6. Rule/Script-Based Evaluation
  7. Model-Based Evaluation
  8. Evaluation Limitations
  9. Methodology
  10. Complex Content-Format Constraints
  11. Basic Instructions Construction
  12. Constraints System Construction
  13. Complex Instructions Construction
  14. Logical Workflow Control
  15. Data Construction Guidelines
  16. ...and 32 more sections

Figures (19)

  • Figure 1: Framework for Complex Instructions Generation.
  • Figure 2: Distribution of Constraints System.
  • Figure 3: Impact of Tool-Use Chain Length on TSR.
  • Figure 4: Impact of Nested Workflows on TSR.
  • Figure 5: Impact of Implicit Tool Invocation on TSR.
  • ...and 14 more figures