Q&A In Financial Queries Using Zero-Shot Learning with LLM for Novel Task Understanding

Abstract

For understanding novel tasks, Zero-Shot Learning (ZSL) in combination with Large Language Models (LLMs) exhibits immense potential. By solely depending on task descriptions or guidelines provided in natural language, LLMs can deduce solutions without requiring explicit training data. For instance, an LLM could be assigned the task of summarizing a newly introduced scientific principle or responding to inquiries on an unfamiliar subject. The model's capability to understand tasks from linguistic indicators and apply pre-acquired knowledge is what makes ZSL particularly effective. Despite these advancements, challenges persist in implementing ZSL with LLMs for new task comprehension. Performance inconsistencies arise when novel tasks significantly differ from training data. Additionally, errors or irrelevant outputs may occur due to misinterpretations. Addressing biases in training data, ensuring output consistency, and enhancing interpretability remain crucial areas for further research.