Spark-LLM-Eval: A Distributed Framework for Statistically Rigorous Large Language Model Evaluation

• URL: http://arxiv.org/abs/2603.28769v1
• Date: Sun, 18 Jan 2026 04:34:39 GMT
• Title: Spark-LLM-Eval: A Distributed Framework for Statistically Rigorous Large Language Model Evaluation
• Abstract summary: We present Spark-LLM-Eval, a distributed evaluation framework built on Apache Spark.<n>The framework and all evaluation code are available as open source.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Evaluating large language models at scale remains a practical bottleneck for many organizations. While existing evaluation frameworks work well for thousands of examples, they struggle when datasets grow to hundreds of thousands or millions of samples. This scale is common when assessing model behavior across diverse domains or conducting comprehensive regression testing. We present Spark-LLM-Eval, a distributed evaluation framework built natively on Apache Spark. The system treats evaluation as a data-parallel problem, partitioningexamplesacrossexecutorsandaggregatingresultswithproperstatistical accounting. Beyond raw throughput, we emphasize statistical rigor: every reported metric includes bootstrap confidence intervals, and model comparisons come with appropriate significance tests (paired t-tests, McNemar's test, or Wilcoxon signed-rank, depending on the metric type). The framework also addresses the cost problem inherent in LLM evaluation through content-addressable response caching backed by Delta Lake, which allows iterating on metric definitions without re-running inference. We describe the system architecture, the statistical methodology, and report benchmark results showing linear scaling with cluster size. The framework and all evaluation code are available as open source.

Related papers

• CollabEval: Statistically Efficient Collaborative Model Evaluation via Matrix Completion [65.53153291902454]
  Collaborative Evaluation (CollabEval) is a simple, effective, and principled method for exploiting dependencies between historical runs of different models on the same tasks to improve statistical efficiency.<n>We build a low-rank approximation of the score matrix, and use the reconstructed values to guarantee unbiased estimates of the true evaluation metric mean.<n>We find that CollabEval substantially reduces the mean confidence interval size, and the mean squared error of the point estimate, compared to baseline methods at the same annotation budget.
  arXiv  Detail & Related papers  (2026-07-06T13:22:04Z)
• A Theoretical Framework for Statistical Evaluability of Generative Models [57.9316356505791]
  We introduce a theoretical framework for evaluating generative models and establish evaluability results for commonly used metrics.<n>We study two categories of metrics: test-based metrics, including integral probability metrics (IPMs) and Rényi divergences.<n>We show that IPMs with respect to any bounded test class can be evaluated from finite samples up to multiplicative and additive approximation errors.<n>In contrast, Rényi and KL divergences are not evaluable from finite samples, as their values can be critically determined by rare events.
  arXiv  Detail & Related papers  (2026-04-07T01:53:59Z)
• Beyond LLM-as-a-Judge: Deterministic Metrics for Multilingual Generative Text Evaluation [20.309826321619482]
  Large Language Models (LLMs) are increasingly adopted as automated judges for evaluating generated text.<n>We propose textbftextit OmniScore, a family of complementary, deterministic learned metrics.<n>We trained the models large-scale synthetic supervision ($sim$564k instances, in textbf107 languages) and evaluated using 8,617 manually annotated instances.
  arXiv  Detail & Related papers  (2026-04-06T18:36:54Z)
• KG-EDAS: A Meta-Metric Framework for Evaluating Knowledge Graph Completion Models [0.0]
  A major challenge in evaluating Knowledge Graphs (KGs) is comparing their performance across multiple datasets and metrics.<n>We propose KG Evaluation based on Distance from Average Solution (EDAS) to integrate multi-metric, multi-dataset performance into a unified ranking.<n>EDAS offers a global perspective that supports more informed model selection and promotes fairness in cross-dataset evaluation.
  arXiv  Detail & Related papers  (2025-08-21T08:37:35Z)
• CompassVerifier: A Unified and Robust Verifier for LLMs Evaluation and Outcome Reward [50.97588334916863]
  We develop CompassVerifier, an accurate and robust lightweight verifier model for evaluation and outcome reward.<n>It demonstrates multi-domain competency spanning math, knowledge, and diverse reasoning tasks, with the capability to process various answer types.<n>We introduce VerifierBench benchmark comprising model outputs collected from multiple data sources, augmented through manual analysis of metaerror patterns to enhance CompassVerifier.
  arXiv  Detail & Related papers  (2025-08-05T17:55:24Z)
• AllMetrics: A Unified Python Library for Standardized Metric Evaluation and Robust Data Validation in Machine Learning [2.325084918639609]
  We introduce AllMetrics, an open-source unified Python library designed to standardize metric evaluation across diverse machine learning tasks.<n>The library implements class-specific reporting for multi-class tasks through parameters to cover all use cases.<n>Various datasets from domains like healthcare, finance, and real estate were applied to our library and compared with Python, Matlab, and R components.
  arXiv  Detail & Related papers  (2025-05-21T18:36:05Z)
• SCORE: Systematic COnsistency and Robustness Evaluation for Large Language Models [4.875712300661656]
  We present SCORE ($mathbfS$ystematic $mathbfCO$nsistency and $mathbfR$obustness $mathbfE$valuation), a comprehensive framework for non-adversarial evaluation of Large Language Models.<n>The SCORE framework evaluates models by repeatedly testing them on the same benchmarks in various setups to give a realistic estimate of their accuracy and consistency.
  arXiv  Detail & Related papers  (2025-02-28T19:27:29Z)
• How to Evaluate Entity Resolution Systems: An Entity-Centric Framework with Application to Inventor Name Disambiguation [1.7812428873698403]
  We propose an entity-centric data labeling methodology that integrates with a unified framework for monitoring summary statistics. These benchmark data sets can then be used for model training and a variety of evaluation tasks.
  arXiv  Detail & Related papers  (2024-04-08T15:53:29Z)
• Regression-aware Inference with LLMs [52.764328080398805]
  We show that an inference strategy can be sub-optimal for common regression and scoring evaluation metrics. We propose alternate inference strategies that estimate the Bayes-optimal solution for regression and scoring metrics in closed-form from sampled responses.
  arXiv  Detail & Related papers  (2024-03-07T03:24:34Z)
• Distributed Collapsed Gibbs Sampler for Dirichlet Process Mixture Models in Federated Learning [0.22499166814992444]
  This paper proposes a new distributed Markov Chain Monte Carlo (MCMC) inference method for DPMMs (DisCGS) using sufficient statistics. Our approach uses the collapsed Gibbs sampler and is specifically designed to work on distributed data across independent and heterogeneous machines. For instance, with a dataset of 100K data points, the centralized algorithm requires approximately 12 hours to complete 100 iterations while our approach achieves the same number of iterations in just 3 minutes.
  arXiv  Detail & Related papers  (2023-12-18T13:16:18Z)
• Bring Your Own Data! Self-Supervised Evaluation for Large Language Models [52.15056231665816]
  We propose a framework for self-supervised evaluation of Large Language Models (LLMs) We demonstrate self-supervised evaluation strategies for measuring closed-book knowledge, toxicity, and long-range context dependence. We find strong correlations between self-supervised and human-supervised evaluations.
  arXiv  Detail & Related papers  (2023-06-23T17:59:09Z)
• Variable Importance Matching for Causal Inference [73.25504313552516]
  We describe a general framework called Model-to-Match that achieves these goals. Model-to-Match uses variable importance measurements to construct a distance metric. We operationalize the Model-to-Match framework with LASSO.
  arXiv  Detail & Related papers  (2023-02-23T00:43:03Z)

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.