Samsung is overcoming limitations of existing benchmarks to better assess the real-world productivity of AI models in enterprise settings. The new system, developed by Samsung Research and named TRUEBench, aims to address the growing disparity between theoretical AI performance and its actual utility in the workplace.
As businesses worldwide accelerate their adoption of large language models (LLMs) to improve their operations, a challenge has emerged: how to accurately gauge their effectiveness. Many existing benchmarks focus on academic or general knowledge tests, often limited to English and simple question and answer formats. This has created a gap that leaves enterprises without a reliable method for evaluating how an AI model will perform on complex, multilingual, and context-rich business tasks.
Samsung’s TRUEBench, short for Trustworthy Real-world Usage Evaluation Benchmark, has been developed to fill this void. It provides a comprehensive suite of metrics that assesses LLMs based on scenarios and tasks directly relevant to real-world corporate environments. The benchmark draws upon Samsung’s own extensive internal enterprise use of AI models, ensuring the evaluation criteria are grounded in genuine workplace demands.
The framework evaluates common enterprise functions such as creating content, analysing data, summarising lengthy documents, and translating materials. These are broken down into 10 distinct categories and 46 sub-categories, providing a granular view of an AI’s productivity capabilities.
“Samsung Research brings deep expertise and a competitive edge through its real-world AI experience,” said Paul (Kyungwhoon) Cheun, CTO of the DX Division at Samsung Electronics and Head of Samsung Research. “We expect TRUEBench to establish evaluation standards for productivity.”
To tackle the limitations of older benchmarks, TRUEBench is built upon a foundation of 2,485 diverse test sets spanning 12 different languages and supporting cross-linguistic scenarios. This multilingual approach is critical for global corporations where information flows across different regions. The test materials themselves reflect the variety of workplace requests, ranging from brief instructions of just eight characters to the complex analysis of documents exceeding 20,000 characters.
Samsung recognised that in a real business context, a user’s full intent is not always explicitly stated in their initial prompt. The benchmark is therefore designed to assess an AI model’s ability to understand and fulfil these implicit enterprise needs, moving beyond simple accuracy to a more nuanced measure of helpfulness and relevance.
To achieve this, Samsung Research developed a unique collaborative process between human experts and AI to create the productivity scoring criteria. Initially, human annotators establish the evaluation standards for a given task. An AI then reviews these standards, checking for potential errors, internal contradictions, or…
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