ChineseStressBench: A Chinese Evaluation Benchmark for High-Pressure Complex Tasks in Real-World Work Scenarios

ChineseStressBench is a Chinese large language model (LLM) evaluation benchmark designed for real-world work scenarios. Its core focus is on whether models will cause "problematic outcomes" (such as misleading outputs, key information omissions, logical confusion, etc.) in high-pressure complex tasks. The project aims to address the gap in existing evaluations that only focus on the upper limit of model capabilities while ignoring reliability in real scenarios. Through task designs that closely mimic actual work, it promotes the transformation of LLMs from "usable" to "user-friendly".

Existing LLM evaluations (such as GLUE, college entrance exam question tests) mostly focus on what models "can do", but rarely pay attention to errors that may lead to serious consequences in real work scenarios. Addressing this pain point, the core philosophy of ChineseStressBench is to test whether models will cause "problematic outcomes" in high-pressure, complex tasks close to real work scenarios—including obvious errors, misleading outputs, key information omissions, and logical confusion under complex constraints.

Task Design Principles

1. Authenticity: Derived from real Chinese work scenarios such as official document processing and business communication, requiring understanding of complex contexts, compliance with norms, and decision-making under multiple constraints.
2. Pressure Cumulativeness: Through multi-tasking with tight timelines and complex dependencies, testing the model's attention allocation and logical consistency.
3. Practicality: Focusing on the standardization of output formats, appropriateness of expression, and compliance with industry practices.

Evaluation Methodology

Adopting a multi-dimensional evaluation system, the core indicator is the "problematic outcome rate" (the proportion of outputs that may cause actual work issues), and establishing a grading standard for error severity to help developers understand risk distribution.

Typical Evaluation Scenarios

• Multi-document Information Integration: Extracting and integrating information from multiple source documents, testing the ability to filter, resolve conflicts, and understand long contexts.
• Temporal Logical Reasoning: Handling timelines, deadline calculations, and dependency sorting—common in project management and schedule planning.
• Norm Compliance and Format Output: Strictly following format norms and terminology standards, applicable to scenarios like official document writing and contract drafting.
• Boundary Case Handling: Testing robustness under abnormal inputs such as ambiguous queries and conflicting instructions, examining whether the model actively clarifies instead of blindly generating wrong answers.

Chinese-Specific Considerations

• Focusing on language characteristic challenges such as Chinese ambiguity, idioms and allusions, and professional terminology.
• Examining cultural adaptability in Chinese contexts, such as the appropriateness of expression in business/social situations.

Insights for Model Development

• Models that perform well in conventional evaluations may encounter issues in high-pressure complex scenarios, reminding developers to prioritize the robustness of real-world scenarios.
• Analysis of error cases helps improve training data, optimize architectures, and refine prompt strategies.

Summary

ChineseStressBench provides a unique perspective, focusing on the lower limit of model reliability rather than the upper limit of capabilities. It is of great significance for promoting the practical application of LLMs. As AI becomes more widespread in production environments, such evaluation benchmarks close to real scenarios will become increasingly important.

Limitations

• It is difficult for evaluation scenarios to fully replicate all the complexities of real work environments.
• Subjective evaluations such as expression appropriateness have biases.

Future Outlook

• Expanding more industry scenarios.
• Introducing dynamic task generation mechanisms.
• Exploring automated error severity assessment to enhance the comprehensiveness and objectivity of evaluations.