Papers with weighted combination
Diverse Few-Shot Text Classification with Multiple Metrics (N18-1)
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Mo Yu, Xiaoxiao Guo, Jinfeng Yi, Shiyu Chang, Saloni Potdar, Yu Cheng, Gerald Tesauro, Haoyu Wang, Bowen Zhou
| Challenge: | Existing methods for few-shot learning are insufficient to capture task variations in natural language domains. |
| Approach: | They propose an adaptive metric learning approach that automatically determines the best weighted combination from a set of metrics obtained from meta-training tasks for a newly seen few-shot task. |
| Outcome: | The proposed method performs favorably against state-of-the-art few shot learning algorithms on real-world sentiment analysis and dialog intent classification datasets. |
Efficient Ensemble for Fine-tuning Language Models on Multiple Datasets (2025.acl-long)
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| Challenge: | Existing methods for fine-tuning language models are efficient when adapting to a single dataset. |
| Approach: | They propose to use an ensemble method for fine-tuning a language model to multiple datasets instead of a single adapter per task. |
| Outcome: | The proposed method improves performance on multiple datasets while preserving low-rank adaptation properties. |