BenchHub: A Unified Benchmark Suite for Holistic and Customizable LLM Evaluation

Eunsu Kim, Haneul Yoo, Guijin Son, Hitesh Laxmichand Patel, Amit Agarwal, Alice Oh

Keywords: LLM evaluation, Benchmark Mixture

TL;DR: We introduce BenchHub, a dynamic benchmark repository that enables researchers and developers to evaluate LLMs more effectively and customize evaluations to fit their specific domains or use cases.

Abstract: As large language models (LLMs) continue to advance, the need for up-to-date and well-organized benchmarks becomes increasingly critical. However, many existing datasets are scattered, difficult to manage, and make it challenging to perform evaluations tailored to specific needs or domains, despite the growing importance of domain-specific models in areas such as math or code. In this paper, we introduce BenchHub, a dynamic benchmark repository that empowers researchers and developers to evaluate LLMs effectively, with a focus on Korean and English. BenchHub aggregates and automatically classifies benchmark datasets from diverse domains, integrating 839k questions across 54 benchmarks. It is designed to support continuous updates and scalable data management, enabling flexible and customizable evaluation tailored to various domains or use cases. Through extensive experiments with various LLM families, we demonstrate that model performance varies significantly across domain-specific subsets, emphasizing the importance of domain-aware benchmarking. Furthermore, we extend BenchHub into 10 languages spanning resource levels. We believe BenchHub can encourage better dataset reuse, more transparent model comparisons, and easier identification of underrepresented areas in existing benchmarks, offering a critical infrastructure for advancing LLM evaluation research.

Primary Area: datasets and benchmarks

Submission Number: 4542