Papers › Differentiable Prompt Makes Pre-trained Language Models Better Few-shot Learners
Differentiable Prompt Makes Pre-trained Language Models Better Few-shot Learners
Ningyu Zhang, Luoqiu Li, Xiang Chen, Shumin Deng, Zhen Bi, Chuanqi Tan, Fei Huang, Huajun Chen
Large-scale pre-trained language models have contributed significantly to natural language processing by demonstrating remarkable abilities as few-shot learners. However, their effectiveness depends mainly on scaling the model parameters and prompt design, hindering their implementation in most real-world applications. This study proposes a novel pluggable, extensible, and efficient approach named DifferentiAble pRompT (DART), which can convert small language models into better few-shot learners without any prompt engineering. The main principle behind this approach involves reformulating potential natural language processing tasks into the task of a pre-trained language model and differentially optimizing the prompt template as well as the target label with backpropagation. Furthermore, the proposed approach can be: (i) Plugged to any pre-trained language models; (ii) Extended to widespread classification tasks. A comprehensive evaluation of standard NLP tasks demonstrates that the proposed approach achieves a better few-shot performance. Code is available in https://github.com/zjunlp/DART.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Few-Shot Learning | CR | DART | Acc | 91.8(0.5) | #1 of 1 | Archive leaderboard | report |
| Few-Shot Learning | GLUE QQP | DART | F1-score | 67.8(3.2) | #1 of 1 | Archive leaderboard | report |
| Few-Shot Learning | MR | DART | Acc | 88.2(1.0) | #1 of 1 | Archive leaderboard | report |
| Few-Shot Learning | MRPC | DART | F1-score | 78.3(4.5) | #1 of 1 | Archive leaderboard | report |
| Few-Shot Learning | SST-2 Binary classification | DART | Acc | 93.5(0.5) | #1 of 1 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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