GRAFITE: Generative Regression Analysis Framework for Issue Tracking and Evaluation

• URL: http://arxiv.org/abs/2603.18173v1
• Date: Wed, 18 Mar 2026 18:14:21 GMT
• Title: GRAFITE: Generative Regression Analysis Framework for Issue Tracking and Evaluation
• Abstract summary: Large language models (LLMs) are largely motivated by their performance on popular topics and benchmarks at the time of their release.<n>Over time, contamination occurs due to significant exposure of benchmark data during training.<n>We present GRAFITE, a continuous LLM evaluation platform through a comprehensive system for maintaining and evaluating model issues.
• Score: 4.874843513834051
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Large language models (LLMs) are largely motivated by their performance on popular topics and benchmarks at the time of their release. However, over time, contamination occurs due to significant exposure of benchmark data during training. This poses a risk of model performance inflation if testing is not carefully executed. To address this challenge, we present GRAFITE, a continuous LLM evaluation platform through a comprehensive system for maintaining and evaluating model issues. Our approach enables building a repository of model problems based on user feedback over time and offers a pipeline for assessing LLMs against these issues through quality assurance (QA) tests using LLM-as-a-judge. The platform enables side-by-side comparison of multiple models, facilitating regression detection across different releases. The platform is available at https://github.com/IBM/grafite. The demo video is available at www.youtube.com/watch?v=XFZyoleN56k.

Related papers

• Debiased Model-based Representations for Sample-efficient Continuous Control [36.705558101460205]
  We propose Debiased model-based Representations for Q-learning, tagged DR.Q algorithm.<n> DR.Q explicitly maximizes the mutual information between the representations of the current state-action pair and the next state.<n>Our results show DR.Q can match or surpass recent strong baselines, sometimes outperforming them by a large margin.
  arXiv  Detail & Related papers  (2026-05-12T08:02:34Z)
• Evaluating from Benign to Dynamic Adversarial: A Squid Game for Large Language Models [57.33350664910483]
  We introduce Squid Game, a dynamic and adversarial evaluation environment with resource-constrained and asymmetric information settings.<n>We evaluate over 50 LLMs on Squid Game, presenting the largest behavioral evaluation study of general LLMs on dynamic adversarial scenarios.
  arXiv  Detail & Related papers  (2025-11-12T06:06:29Z)
• MINERVA: Evaluating Complex Video Reasoning [72.12644008002566]
  We provide a new video reasoning dataset called MINERVA for modern multimodal models.<n>Our dataset is multimodal, diverse in terms of video domain and length, and consists of complex multi-step questions.<n>We perform fine-grained error analysis to identify common failure modes across various models, and create a taxonomy of reasoning errors.
  arXiv  Detail & Related papers  (2025-05-01T17:41:49Z)
• Do Large Language Model Benchmarks Test Reliability? [66.1783478365998]
  We investigate how well current benchmarks quantify model reliability.<n>Motivated by this gap in the evaluation of reliability, we propose the concept of so-called platinum benchmarks.<n>We evaluate a wide range of models on these platinum benchmarks and find that, indeed, frontier LLMs still exhibit failures on simple tasks.
  arXiv  Detail & Related papers  (2025-02-05T18:58:19Z)
• Leveraging Online Olympiad-Level Math Problems for LLMs Training and Contamination-Resistant Evaluation [55.21013307734612]
  AoPS-Instruct is a dataset of more than 600,000 high-quality QA pairs.<n>LiveAoPSBench is an evolving evaluation set with timestamps, derived from the latest forum data.<n>Our work presents a scalable approach to creating and maintaining large-scale, high-quality datasets for advanced math reasoning.
  arXiv  Detail & Related papers  (2025-01-24T06:39:38Z)
• Generating Unseen Code Tests In Infinitum [1.0674604700001968]
  We present a method for creating benchmark variations that generalize across coding tasks and programming languages. We implement one benchmark, called textitauto-regression, for the task of text-to-code generation in Python.
  arXiv  Detail & Related papers  (2024-07-29T08:11:20Z)
• Evaluating Language Model Context Windows: A "Working Memory" Test and Inference-time Correction [10.428174043080622]
  Large language models are prominently used in real-world applications, often tasked with reasoning over large volumes of documents. We propose SWiM, an evaluation framework that addresses the limitations of standard tests. We also propose medoid voting, a simple, but effective training-free approach that helps alleviate this effect.
  arXiv  Detail & Related papers  (2024-07-04T05:46:20Z)
• LiveBench: A Challenging, Contamination-Limited LLM Benchmark [93.57775429120488]
  We release LiveBench, the first benchmark that contains frequently-updated questions from recent information sources.<n>We evaluate many prominent closed-source models, as well as dozens of open-source models ranging from 0.5B to 405B in size.<n>Questions are added and updated on a monthly basis, and we release new tasks and harder versions of tasks over time.
  arXiv  Detail & Related papers  (2024-06-27T16:47:42Z)
• Automating Dataset Updates Towards Reliable and Timely Evaluation of Large Language Models [81.27391252152199]
  Large language models (LLMs) have achieved impressive performance across various natural language benchmarks. We propose to automate dataset updating and provide systematic analysis regarding its effectiveness. There are two updating strategies: 1) mimicking strategy to generate similar samples based on original data, and 2) extending strategy that further expands existing samples.
  arXiv  Detail & Related papers  (2024-02-19T07:15:59Z)
• Investigating Data Contamination in Modern Benchmarks for Large Language Models [27.479260572913724]
  Recent observations have underscored a disparity between the inflated benchmark scores and the actual performance of LLMs. We study data contamination by proposing two methods tailored for both open-source and proprietary LLMs. We find that certain commercial LLMs could surprisingly guess the missing option in various test sets.
  arXiv  Detail & Related papers  (2023-11-16T11:03:04Z)

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.