Papers with Model-Agnostic Meta-Learning
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. |
PubMedCLIP: How Much Does CLIP Benefit Visual Question Answering in the Medical Domain? (2023.findings-eacl)
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| Challenge: | Medical visual question answering is a multimodal task that requires a system to understand both medical images and textual questions and infer associations between them. |
| Approach: | They propose a fine-tuned version of CLIP for the medical domain based on PubMed articles. |
| Outcome: | The proposed model improves accuracy up to 3% on two MedVQA benchmark datasets. |
Towards Cross-Lingual Audio Abuse Detection in Low-Resource Settings with Few-Shot Learning (2025.coling-main)
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| Challenge: | Online abusive content detection, particularly in low-resource settings, remains underexplored. |
| Approach: | They propose to use pre-trained audio representations to detect abusive language in Indian languages using Few Shot Learning (FSL) . |
| Outcome: | The proposed model can be used to classify abusive language in 10 languages using the ADIMA dataset with FSL. |
Few-Shot Representation Learning for Out-Of-Vocabulary Words (P19-1)
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| Challenge: | Existing methods for learning word embedding assume there are enough occurrences for each word in the corpus to accurately estimate the representation of words. |
| Approach: | They propose to fit a representation function to predict an oracle embedding vector based on limited contexts. |
| Outcome: | The proposed model outperforms existing methods in constructing an accurate embedding for OOV words and improves downstream tasks when the embeddable is utilized. |
Personalizing Dialogue Agents via Meta-Learning (P19-1)
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| Challenge: | Existing personalized dialogue models use human designed persona descriptions to improve dialogue consistency. |
| Approach: | They propose to extend Model-Agnostic Meta-Learning (MAML) to personalized dialogue learning without using persona descriptions. |
| Outcome: | The proposed model outperforms baseline models in terms of human-evaluated fluency and consistency on a persona-chat dataset. |