Papers with generative large pre-trained language model

1 papers
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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