Academic Journal
Prompt-MAML: Model-Agnostic Meta-in-Context Learning for Major Depressive Disorder Classification
| Τίτλος: | Prompt-MAML: Model-Agnostic Meta-in-Context Learning for Major Depressive Disorder Classification |
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| Συγγραφείς: | Lifelo, Zita, Author, Ding, Jianguo, 1968-, Author, Wang, Zongjie, Author, Shi, Feifei, Author, Ning, Huansheng, Author, Dhelim, Sahraoui, Author |
| Συνεισφορές: | Lifelo, Zita, Author, Ding, Jianguo, 1968-, Author, Wang, Zongjie, Author, Shi, Feifei, Author, Ning, Huansheng, Author, Dhelim, Sahraoui, Author |
| Πηγή: | Tsinghua Science and Technology. 31(5):2597-2610 |
| Θεματικοί όροι: | in-context learning, large language model, major depressive disorder detection, model-agnostic meta learning, multimodality |
| Περιγραφή: | The classification of major depressive disorders (MDDs) is a challenging task in clinical practice, especially in low-resource scenarios where generalization is essential for effective adaptation. Recent progress in meta-training large language models (LLMs) via in-context learning (ICL) offers promise for robust adaptation to unseen tasks without parameter updates. However, existing methods rely on multitask fine-tuning and do not fully exploit the optimization advantages of model-agnostic meta learning (MAML) techniques, limiting their generalization. This study proposes prompt-MAML, a novel method for meta-training LLMs that enhances multimodal ICL for classifying MDD tasks. The method integrates audio-textual features through a transformer-based cross-modal alignment module and incorporates bi-level optimization to learn generalizable model parameters that adapt well to unseen tasks. Extensive experiments demonstrate that prompt-MAML outperforms strong baseline models by an average improvement in macro-F1 of +4% on seen domains, +3% on unseen domains, and +3% in few-shot settings, demonstrating robustness and effectiveness in data-scarce and cross-domain clinical scenarios. Additionally, exploration depth is shown to play a key role in task performance, and further analysis of task complexity, modality, and optimiser configurations highlights critical design considerations for meta-training LLMs. |
| Περιγραφή αρχείου: | electronic |
| Σύνδεσμος πρόσβασης: | https://urn.kb.se/resolve?urn=urn:nbn:se:bth-29927 https://doi.org/10.26599/TST.2025.9010116 |
| Βάση Δεδομένων: | SwePub |
| ISSN: | 10070214 18787606 |
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| DOI: | 10.26599/TST.2025.9010116 |