Papers by Yulun Du
StoryWars: A Dataset and Instruction Tuning Baselines for Collaborative Story Understanding and Generation (2023.acl-long)
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| Challenge: | Understanding and generating collaborative stories remains an underexplored area due to the lack of open-domain corpora. |
| Approach: | They propose to use a dataset of 40,000 collaborative stories written by 9,400 different authors from an online platform to generate a multi-task benchmark. |
| Outcome: | The proposed model achieves the best performance on fully-supervised, few-shot, and zero-shot scenarios while achieving the best results on the fully-supervised tasks. |
GPS: Genetic Prompt Search for Efficient Few-Shot Learning (2022.emnlp-main)
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| Challenge: | Pretrained language models are often finetuned for downstream tasks, which has been shown to improve performance over non-pretrained models. |
| Approach: | They propose a genetic algorithm to automatically search for the best prompt for few-shot learning with pretrained language models by gradient-free algorithm. |
| Outcome: | Experiments on diverse datasets show that the proposed method outperforms manual prompts by 2.6 points. |
ZeroPrompt: Scaling Prompt-Based Pretraining to 1,000 Tasks Improves Zero-Shot Generalization (2022.findings-emnlp)
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| Challenge: | a recent study shows that task scaling can be an efficient alternative to model scaling. |
| Approach: | They propose a multitask pretraining approach ZeroPrompt for zero-shot generalization . they focus on task scaling and zero-shooting to improve model performance . |
| Outcome: | The proposed approach improves zero-shot generalization efficiency by 30 times with task scaling. |