LiFT: Does Instruction Fine-Tuning Improve In-Context Learning for Longitudinal Modelling by Large Language Models?

LiFT is a new instruction fine-tuning framework designed to improve in-context learning for large language models on longitudinal NLP tasks, which require reasoning over temporally ordered text. It uses a curriculum that progressively increases temporal difficulty, incorporating few-shot structure and temporal conditioning, consistently outperforming base models across various datasets and parameter sizes.