Search for: All records

Large Language Models (LLMs) heavily rely on high-quality training data, making data valuation crucial for optimizing model performance, especially when working within a limited budget. In this work, we aim to offer a third-party data valuation approach that benefits both data providers and model developers. We introduce a linearized future influence kernel (LinFiK), which assesses the value of individual data samples in improving LLM performance during training. We further propose ALinFiK, a learning strategy to approximate LinFiK, enabling scalable data valuation. Our comprehensive evaluations demonstrate that this approach surpasses existing baselines in effectiveness and efficiency, demonstrating significant scalability advantages as LLM parameters increase. 

This study focuses on improving the preparation of spectral data for machine learning. It does so by conducting a case study that involves matching an airborne gamma-ray spectral survey of the San Francisco Bay area to geological classifications provided by the United States Geological Survey (Graymer et al., 2006).Our investigation has revealed three key approaches for enhancing accuracy in this task:1) eliminating extraneous data segments unrelated to the main task,2) augmenting minority classes to improve class balances,and 3) merging inconsistent classes.By incorporating these methods, we were able to achieve a significant increase in classification accuracy. Specifically, we increased the accuracy from an initial 40.8% to approximately 72.7%. We plan to continue our work to further enhance performance, with the goal of extending the applicability of these methods to other data types and tasks. One potential future application is the detection of rare earth elements from aerial surveys.