Kshitiz Regmi on LinkedIn: Proving Test Set Contamination in Black Box Models
Using test data during training is a critical form of data leakage, especially for large language models (LLMs). This practice can lead to models appearing to perform exceptionally well during evaluation. The issue becomes particularly problematic when dealing with open-source or internet data, as the boundaries between training and test datasets can become blurred. Therefore, it is essential to understand the distribution of training data to prevent test data contamination.
If test data leaks into the training set, the performance metrics may not accurately reflect the model's true capabilities. This overestimation of performance can result in misguided decisions based on misleading metrics. Businesses relying on LLMs for crucial applications require accurate assessments of model performance to avoid incorrect decisions, financial losses, and reputational damage.
Research on Test Set Contamination
Recent research from Stanford and Columbia universities explores ways to prove test set contamination in language models without access to pretraining data or model weights. This research opens up new possibilities for identifying and addressing test set contamination in LLMs. By resolving these issues, businesses can ensure that their solutions are evaluated more accurately, leading to better decision-making processes.
Ensuring that test data is not used during training is a fundamental aspect of building trustworthy and effective machine learning models.
AI and Data Science Community Collaboration
Participating in events like the "AI CONFERENCE For A Prosperous Nepal" organized by the Ministry of Education, Science, and Technology [MoEST] with partners such as the Nepal Academy of Science and Technology (NAST), The Asia Foundation, Robotics Association of Nepal, Frost Digital Ventures, UKaid, UNDP, USAID, Fusemachines, and others, is a fantastic opportunity to witness government initiatives implementing AI for the greater good.
Collaborating with experts like Rojesh M. Shikhrakar, Sijan Shrestha, and many more at such conferences is invaluable in driving advancements in AI and data science fields.
