GRAFITE: Generative Regression Analysis Framework for Issue Tracking and Evaluation
arXiv:2603.18173v1 Announce Type: new 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=XFZyoleN
arXiv:2603.18173v1 Announce Type: new 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.
Executive Summary
GRAFITE, a novel Generative Regression Analysis Framework for Issue Tracking and Evaluation, is proposed to address the challenge of model performance inflation in large language models (LLMs) due to contamination from significant exposure of benchmark data. The platform utilizes a comprehensive system for maintaining and evaluating model issues, enabling the building of a repository of model problems and providing a pipeline for assessing LLMs against these issues through quality assurance (QA) tests. The GRAFITE platform facilitates side-by-side comparison of multiple models, facilitating regression detection across different releases. The implications of this work are significant, as it enables more accurate and reliable evaluation of LLMs over time, ultimately enhancing their performance and trustworthiness. The platform is available on GitHub, and a demo video is available on YouTube.
Key Points
- ▸ GRAFITE is designed to address the challenge of model performance inflation in LLMs
- ▸ The platform utilizes a comprehensive system for maintaining and evaluating model issues
- ▸ GRAFITE enables the building of a repository of model problems and provides a pipeline for assessing LLMs against these issues
- ▸ The platform facilitates side-by-side comparison of multiple models, facilitating regression detection across different releases
Merits
Strength in Addressing Model Performance Inflation
GRAFITE tackles the critical issue of model performance inflation, which is a significant concern in the development and deployment of LLMs.
Comprehensive System for Issue Tracking and Evaluation
The platform's comprehensive system for maintaining and evaluating model issues enables more accurate and reliable evaluation of LLMs.
Flexibility in Model Comparison and Regression Detection
GRAFITE facilitates side-by-side comparison of multiple models, enabling regression detection across different releases.
Demerits
Limited Dataset and Scalability
The platform's effectiveness may be limited by the size and diversity of the dataset used for training and evaluation.
Dependence on Quality Assurance (QA) Tests
The platform's accuracy relies heavily on the quality of QA tests, which may be subject to variation and bias.
Expert Commentary
The GRAFITE platform is a significant contribution to the field of natural language processing and machine learning, as it addresses the critical issue of model performance inflation in LLMs. The platform's comprehensive system for maintaining and evaluating model issues enables more accurate and reliable evaluation of LLMs, ultimately enhancing their performance and trustworthiness. While the platform has some limitations, such as its dependence on QA tests and limited dataset scalability, its implications are significant and far-reaching. As LLMs become increasingly prevalent in various applications, the need for more rigorous evaluation and validation of these models becomes increasingly pressing. GRAFITE provides a valuable tool for researchers and practitioners seeking to address this challenge.
Recommendations
- ✓ Future research should focus on improving the scalability and generalizability of GRAFITE, particularly in terms of dataset size and diversity.
- ✓ The use of GRAFITE should be explored in high-stakes applications such as healthcare and finance, where the accuracy and reliability of LLMs are critical.
