Papers with Prompt choice
CRL-Prompt: Contrastive and Reinforcement Learning for Soft Prompt Tuning for Text Classification (2026.acl-srw)
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| Challenge: | Manual prompt engineering is time-consuming, non-scalable, and brittle, while current auto-prompting techniques are far from maturity. |
| Approach: | They propose a two-stage method for prompt learning of frozen language models, CRL-Prompt, based on soft prompt initialization followed by contrastive and reinforcement-based refinement. |
| Outcome: | The proposed method achieves consistent improvements over baseline prompt tuning strategies, with gains of up to 2.2% while training fewer than 0.25% of model parameters. |