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

• URL: http://arxiv.org/abs/2506.00482v1
• Date: Sat, 31 May 2025 09:24:32 GMT
• Title: BenchHub: A Unified Benchmark Suite for Holistic and Customizable LLM Evaluation
• Authors: Eunsu Kim, Haneul Yoo, Guijin Son, Hitesh Patel, Amit Agarwal, Alice Oh,
• Abstract summary: BenchHub is a dynamic benchmark repository that empowers researchers and developers to evaluate large language models (LLMs) more effectively.<n>It is designed to support continuous updates and scalable data management, enabling flexible and customizable evaluation tailored to various domains or use cases.
• Score: 13.897645524385274
• License: http://creativecommons.org/licenses/by/4.0/
• 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 more effectively. BenchHub aggregates and automatically classifies benchmark datasets from diverse domains, integrating 303K questions across 38 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. 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.

Related papers

• StoryBench: A Dynamic Benchmark for Evaluating Long-Term Memory with Multi Turns [7.60350050736492]
  Long-term memory is essential for large language models to achieve autonomous intelligence.<n>Existing benchmarks face challenges in evaluating knowledge retention and dynamic sequential reasoning.<n>We propose a novel benchmark framework based on interactive fiction games.
  arXiv  Detail & Related papers  (2025-06-16T10:54:31Z)
• General-Reasoner: Advancing LLM Reasoning Across All Domains [64.70599911897595]
  Reinforcement learning (RL) has recently demonstrated strong potential in enhancing the reasoning capabilities of large language models (LLMs)<n>We propose General-Reasoner, a novel training paradigm designed to enhance LLM reasoning capabilities across diverse domains.<n>We train a series of models and evaluate them on a wide range of datasets covering wide domains like physics, chemistry, finance, electronics etc.
  arXiv  Detail & Related papers  (2025-05-20T17:41:33Z)
• Top General Performance = Top Domain Performance? DomainCodeBench: A Multi-domain Code Generation Benchmark [38.14474956762422]
  We introduce DomainCodeBench, a benchmark designed to evaluate large language models (LLMs) across 12 software application domains and 15 programming languages.<n>We find that top general-domain models do not consistently excel in specific application domains.<n>We show that augmenting prompts with domain-specific knowledge improves performance by around 38.17%.
  arXiv  Detail & Related papers  (2024-12-24T17:56:08Z)
• MAmmoTH-VL: Eliciting Multimodal Reasoning with Instruction Tuning at Scale [66.73529246309033]
  multimodal large language models (MLLMs) have shown significant potential in a broad range of multimodal tasks.<n>Existing instruction-tuning datasets only provide phrase-level answers without any intermediate rationales.<n>We introduce a scalable and cost-effective method to construct a large-scale multimodal instruction-tuning dataset with rich intermediate rationales.
  arXiv  Detail & Related papers  (2024-12-06T18:14:24Z)
• BenchmarkCards: Standardized Documentation for Large Language Model Benchmarks [23.263430784766026]
  Large language models (LLMs) are powerful tools capable of handling diverse tasks.<n>Finding suitable benchmarks is difficult given the many available options.<n>We introduce textttBenchmarkCards, an intuitive and validated documentation framework.
  arXiv  Detail & Related papers  (2024-10-16T19:09:02Z)
• TestAgent: A Framework for Domain-Adaptive Evaluation of LLMs via Dynamic Benchmark Construction and Exploratory Interaction [29.72874725703848]
  Large language models (LLMs) are increasingly deployed to various vertical domains.<n>Current evaluation methods rely on static and resource-intensive datasets that are not aligned with real-world requirements.<n>We introduce two key concepts: textbfBenchmark+, which extends the traditional question-answer benchmark into a more flexible strategy-criterion'' format.<n>We propose textbftextscTestAgent, an agent-based evaluation framework that implements these concepts using retrieval-augmented generation and reinforcement learning.
  arXiv  Detail & Related papers  (2024-10-15T11:20:42Z)
• BabelBench: An Omni Benchmark for Code-Driven Analysis of Multimodal and Multistructured Data [61.936320820180875]
  Large language models (LLMs) have become increasingly pivotal across various domains. BabelBench is an innovative benchmark framework that evaluates the proficiency of LLMs in managing multimodal multistructured data with code execution. Our experimental findings on BabelBench indicate that even cutting-edge models like ChatGPT 4 exhibit substantial room for improvement.
  arXiv  Detail & Related papers  (2024-10-01T15:11:24Z)
• DiscoveryBench: Towards Data-Driven Discovery with Large Language Models [50.36636396660163]
  We present DiscoveryBench, the first comprehensive benchmark that formalizes the multi-step process of data-driven discovery. Our benchmark contains 264 tasks collected across 6 diverse domains, such as sociology and engineering. Our benchmark, thus, illustrates the challenges in autonomous data-driven discovery and serves as a valuable resource for the community to make progress.
  arXiv  Detail & Related papers  (2024-07-01T18:58:22Z)
• ERBench: An Entity-Relationship based Automatically Verifiable Hallucination Benchmark for Large Language Models [46.07900122810749]
  Large language models (LLMs) have achieved unprecedented performances in various applications, yet evaluating them is still challenging. We contend that utilizing existing relational databases is a promising approach for constructing benchmarks. We propose ERBench, which uses these integrity constraints to convert any database into an LLM benchmark.
  arXiv  Detail & Related papers  (2024-03-08T12:42:36Z)
• Benchmark Self-Evolving: A Multi-Agent Framework for Dynamic LLM Evaluation [51.99752147380505]
  This paper presents a benchmark self-evolving framework to dynamically evaluate Large Language Models (LLMs) We utilize a multi-agent system to manipulate the context or question of original instances, reframing new evolving instances with high confidence. Our framework widens performance discrepancies both between different models and within the same model across various tasks.
  arXiv  Detail & Related papers  (2024-02-18T03:40:06Z)
• LAMM: Language-Assisted Multi-Modal Instruction-Tuning Dataset, Framework, and Benchmark [81.42376626294812]
  We present Language-Assisted Multi-Modal instruction tuning dataset, framework, and benchmark. Our aim is to establish LAMM as a growing ecosystem for training and evaluating MLLMs. We present a comprehensive dataset and benchmark, which cover a wide range of vision tasks for 2D and 3D vision.
  arXiv  Detail & Related papers  (2023-06-11T14:01:17Z)
• DataPerf: Benchmarks for Data-Centric AI Development [81.03754002516862]
  DataPerf is a community-led benchmark suite for evaluating ML datasets and data-centric algorithms. We provide an open, online platform with multiple rounds of challenges to support this iterative development. The benchmarks, online evaluation platform, and baseline implementations are open source.
  arXiv  Detail & Related papers  (2022-07-20T17:47:54Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.