As artificial intelligence tools become more common in the workplace, their ability to understand and answer questions about large volumes of company data is increasingly important. However, testing these AI systems accurately has been a challenge because real corporate communications are private, and existing test datasets are often too simple to reflect real business complexity. A newly published research paper introduces CorporateBench, a large-scale, human-verified benchmark designed to evaluate how well AI models handle realistic corporate knowledge and communication over time.
Key Takeaways
- CorporateBench (CB) simulates large, evolving corporate communication networks with over 230,000 documents, mimicking real-world complexity.
- CB tests AI models on two main tasks: extracting specific information and querying a knowledge base representing a company’s data.
- The benchmark includes four synthetic companies ranging from 12 to 10,000 employees, providing a range of difficulty levels.
- Evaluations of five leading large language models (LLMs) showed that AI performance drops significantly as the amount of input data grows to realistic corporate scales.
The researchers behind CorporateBench aimed to bridge a critical gap in AI evaluation. Most existing benchmarks either rely on simplified synthetic data or cannot use real company documents due to privacy concerns. CorporateBench addresses this by creating detailed, synthetic but human-validated corporate datasets that evolve over time. This temporal aspect means the data changes logically and consistently, reflecting how real companies grow, change projects, and update their knowledge bases.
To build CorporateBench, the team generated four fictional companies of varying sizes, each with extensive document collections representing emails, reports, and other communications. These documents were drawn from a knowledge base that evolves logically over time, ensuring that facts remain consistent across hundreds of thousands of documents. This design allows the benchmark to test AI models on tasks that require reasoning over large, temporally complex information spaces—something real enterprise AI systems must handle.
The two main tasks in CorporateBench are information extraction and knowledge base querying. Information extraction involves pulling out specific facts from documents, such as dates, names, or project details. Knowledge base querying tests whether an AI can answer questions by reasoning over the entire evolving dataset, similar to how an employee might search for up-to-date company information. By evaluating AI models on these tasks across companies of different sizes, the benchmark reveals how well models scale to realistic corporate environments.
Testing five popular large language models on CorporateBench revealed a notable trend: as the input data size approached the scale of actual companies, AI performance declined significantly. This finding highlights current limitations in AI’s ability to process and reason over massive, temporally evolving datasets—an essential capability for practical business applications.
CorporateBench offers a valuable new tool for AI developers and researchers aiming to improve enterprise-focused language models. By providing a realistic and scalable benchmark, it helps identify where AI systems struggle and guides future improvements to better handle complex corporate information. While CorporateBench uses synthetic data, its careful design ensures that it closely approximates the challenges found in real-world corporate communication networks.
Looking ahead, this research could lead to more robust AI assistants capable of supporting knowledge workers by accurately navigating vast and changing company data. As AI continues to integrate into business workflows, benchmarks like CorporateBench will be crucial for ensuring these systems are reliable and effective at scale.
Based on research published on arXiv by Sil Hamilton, Albert Yu Sun, Oscar J. Romero et al..