Samsung’s TRUEBench: Revolutionizing AI Productivity Evaluation

Samsung is striving to overcome the limitations of current evaluation standards to provide a more accurate picture of AI models’ productivity in real-world work environments. Through a new system developed by Samsung’s research center called TRUEBench, the company aims to address the growing gap between the theoretical performance of AI and its actual usefulness in the workplace.

The Importance of TRUEBench in the Digital World

As companies worldwide accelerate the adoption of large language models to enhance their operations, challenges have emerged in accurately measuring their effectiveness. Many current standards focus on academic or general knowledge tests, often limited to English and simple question-and-answer formats. This has created a gap, leaving companies without a reliable method to evaluate AI model performance in complex, multilingual, and context-rich business tasks.

This is where TRUEBench comes in, offering a comprehensive set of metrics that evaluate language models based on scenarios and tasks directly relevant to real-world work environments. These standards are based on Samsung’s extensive internal use of AI models, ensuring that the evaluation criteria are built on actual business needs.

How TRUEBench Works

The framework assesses common organizational functions such as content creation, data analysis, summarizing lengthy documents, and translating materials. These functions are divided into 10 main categories and 46 subcategories, providing a detailed view of AI productivity capabilities.

TRUEBench relies on 2,485 diverse test sets covering 12 different languages and supporting cross-language scenarios. This multilingual approach is essential for global companies where information flows between different regions. The test materials themselves reflect the diversity of workplace demands, ranging from brief instructions containing only eight characters to complex document analyses exceeding 20,000 characters.

Human-AI Collaboration

To design productivity evaluation standards, Samsung developed a unique collaborative process between human experts and AI. Humans start by defining the evaluation criteria for a specific task. AI then reviews these criteria, looking for potential errors, internal inconsistencies, or unnecessary constraints that might not reflect realistic user expectations. Subsequently, human experts refine the criteria based on AI feedback.

This iterative loop ensures that the final standards are accurate and reflected in high-quality results. This system provides automated evaluation of large language models’ performance accurately and reliably.

Transparency and Global Adoption

To increase transparency and encourage broader adoption, Samsung has made TRUEBench data samples and leaderboards publicly available on the global open-source platform Hugging Face. This allows developers, researchers, and companies to compare the productivity performance of five different AI models simultaneously.

This system provides a comprehensive view of how different models compare in practical tasks. The published data also includes the average length of responses generated by AI, allowing for performance and efficiency comparisons at the same time.

Conclusion

With the launch of TRUEBench, Samsung is not only introducing a new tool but also aiming to change how AI performance is evaluated in the industry. By shifting standards from abstract knowledge to tangible productivity, Samsung’s benchmark can play a role in helping organizations make better decisions about which AI models to integrate into their workflows, bridging the gap between AI capabilities and proven value.