Papers with TRL
Task Refinement Learning for Improved Accuracy and Stability of Unsupervised Domain Adaptation (P19-1)
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| Challenge: | Existing approaches to domain adaptation (DA) require labeled data that can be found in only a handful of domains. |
| Approach: | They propose a task-refinement learning approach to solve pivot detection problems . they propose to train PBLM models with gradually increasing information exposed about each pivot . |
| Outcome: | The proposed approach achieves state-of-the-art accuracy in six domain adaptation setups for sentiment classification. |
Fine-Tuning Language Models on Multiple Datasets for Citation Intention Classification (2024.findings-emnlp)
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Zeren Shui, Petros Karypis, Daniel Karls, Mingjian Wen, Saurav Manchanda, Ellad Tadmor, George Karypis
| Challenge: | Prior research has shown that pretrained language models (PLMs) can achieve state-of-the-art performance on CIC benchmarks. |
| Approach: | They propose a multi-task learning framework that fine-tunes pretrained language models on a dataset of primary interest together with multiple auxiliary CIC datasets to take advantage of additional supervision signals. |
| Outcome: | The proposed framework outperforms current state-of-the-art models on small datasets while aligning with the best-performing model on a large dataset. |