Research & Publications

The abundance of digital documents offers a valuable chance to gain insights into public opinion, social structure, and dynamics. However, the scale and volume of these digital collections makes manual analysis approaches extremely costly and not scalable. In this paper, we study the potential of using automated methods from natural language processing and machine learning, in particular weak supervision strategies, to understand how news influence decision making in society. Besides proposing a weak supervision solution for the task, which replaces manual labeling to a certain extent, we propose an improvement of a recently published economic index. This index is known as economic policy uncertainty (EPU) index and has been shown to correlate to indicators such as firm investment, employment, and excess market returns.

Large language models (LLMs) have recently made significant advances, excelling in tasks like question answering, summarization, and machine translation. However, their enormous size and hardware requirements make them less accessible to many in the machine learning community. This research investigates how variations like model size, pre-training objectives, quantization, low-rank adaptation, prompting, and various hyperparameters influence text classification tasks. The study finds that fine-tuning works better than prompting techniques, discriminatively trained models generally perform better than generatively pre-trained models, and fine-tuning models at 16-bit precision works much better than using 8-bit or 4-bit models.