Papers with multi-task baselines
Decoupling Generalization and Adaptation in Meta-Learning for Large Language Models (2026.acl-short)
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| Challenge: | Adapting large language models to specific downstream tasks requires multi-step fine-tuning with substantial training data, incurring significant computational overhead. |
| Approach: | They propose a framework that separates learning generalizable initializations and adaptation through dedicated parameter spaces. |
| Outcome: | The proposed framework outperforms existing meta-learning and standard multi-task baselines on common-sense reasoning, mathematics, logic, medical and coding benchmarks. |
Multi-Task Learning of Pairwise Sequence Classification Tasks over Disparate Label Spaces (N18-1)
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| Challenge: | Multi-task learning and semi-supervised learning are successful paradigms for learning in scenarios with limited labelled data. |
| Approach: | They propose to induce a joint embedding space between disparate label spaces and learning transfer functions between label embeddments to leverage unlabelled data and auxiliary, annotated datasets. |
| Outcome: | The proposed approach outperforms strong single and multi-task baselines and achieves state of the art on aspect-based and topic-based sentiment analysis. |
Meta-Learning for Effective Multi-task and Multilingual Modelling (2021.eacl-main)
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| Challenge: | Existing studies on multitask and multilingual learning have shown that learning cross-lingual embeddings can benefit multiple tasks and languages. |
| Approach: | They propose a meta-learning approach to learn interactions between tasks and languages . they also investigate the role of different sampling strategies used during meta-learned model . |
| Outcome: | The proposed model improves on five different tasks and six different languages from the XTREME multilingual benchmark dataset. |