LiFT: How to Enable In-Context Learning for Longitudinal Modelling
Longitudinal NLP tasks such as mental health monitoring and stance evolution require modeling temporally ordered text to track persistence and detect change. Such tasks also suffer from data scarcity, often involving rare events and sparsely annotated data. Large Language Models (LLMs) can learn from small amounts of data through in-context learning (ICL), which is particularly important in low-data resource scenarios. However, when it comes to tracking evolving interactions or identifying rare events or changes, LLMs fall short in both zero- and few-shot ICL. To address this limitation, we introduce LiFT, a model-agnostic longitudinal Instruction Fine-Tuning framework that is able to ind...
| Authors | Iqra Ali, Talia Tseriotou, Mahmud Elahi Akhter et al. |
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| Primary category | cs.CL |
| Published | 25 March 2026 |
| Last updated at source | 11 August 2026 |
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