Samsung has unveiled TRUEBench (Trustworthy Real-world Usage Evaluation Benchmark), a proprietary benchmark developed by Samsung Research to evaluate AI productivity.
TRUEBench provides a comprehensive set of metrics to measure how large language models (LLMs) perform in real-world workplace productivity applications. To ensure realistic evaluation, it incorporates diverse dialogue scenarios and multilingual conditions.
Drawing on Samsung’s in-house use of AI for productivity, TRUEBench evaluates commonly used enterprise tasks — such as content generation, data analysis, summarization and translation — across 10 categories and 46 sub-categories. The benchmark ensures reliable scoring with AI-powered automatic evaluation based on criteria that are collaboratively designed and refined by both humans and AI.
“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 and solidify Samsung’s technological leadership.”
Recently, as companies adopt AI for tasks there has been a growing demand for measuring the productivity of LLMs. However, existing benchmarks primarily measure overall performance, are mostly English‑centric, and are limited to single‑turn question‑answer structures. This restricts their ability to reflect actual work environments.
To address these limitations, TRUEBench is composed of a total of 2,485 test sets across 10 categories and 12 languages— while also supporting cross-linguistic scenarios. The test sets examine what AI models can actually solve, and Samsung Research applied test sets ranging from as short as 8 characters to over 20,000 characters, reflecting tasks from simple requests to lengthy document summarization.
To evaluate the performance of AI models, it is important to have clear criteria for judging whether the AI’s responses are correct. In real-world situations, not all user intents may be explicitly stated in the instructions. TRUEBench is designed to enable realistic evaluation by considering not only the accuracy of the answers but also detailed conditions that meet the implicit needs of users.
Samsung Research verified evaluation items through collaboration between humans and AI. First, human annotators create the evaluation criteria, and then the AI reviews it to check for errors, contradictions, or unnecessary constraints. Afterward, human annotators refine the criteria again, repeating this process to apply increasingly precise evaluation standards. Based on these cross-verified criteria, automatic evaluation of AI models is conducted, minimizing subjective bias and ensuring consistency. In addition, for each test, all conditions must be satisfied for the model to pass. This enables more detailed and precise scoring across tasks.
TRUEBench’s data samples and leaderboards are available on the global open-source platform Hugging Face, which allows users to compare a maximum of five models and enables comprehensive AI model performance comparisons at a glance.
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