GRAFITE: Generative Regression Analysis Framework for Issue Tracking and Evaluation
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-jud...
| Authors | Ja Young Lee, Mírian Silva, Mohamed Nasr et al. |
|---|---|
| Primary category | cs.CL |
| Published | 18 March 2026 |
| Last updated at source | 18 March 2026 |
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