Select High-quality Synthetic QA Pairs to Augment Training Data in MRC under the Reward Guidance of Generative Language Models (2024.lrec-main)
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| Challenge: | Existing approaches focus on downstream metrics to select QA pairs, which lack generalization across different datasets. |
| Approach: | They propose a general selection method that uses a large pre-trained language model as a reward model in a Reinforcement Learning framework for the training of the selection agent. |
| Outcome: | The proposed method improves performance on generative and extractive datasets. |
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