Papers with downstream prediction
Spotlighter: Revisiting Prompt Tuning from a Representative Mining View (2025.findings-emnlp)
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| Challenge: | Spotlighter is a lightweight token-selection framework that enhances accuracy and efficiency in prompt tuning. |
| Approach: | They propose a token-selection framework that enhances accuracy and efficiency in prompt tuning by preserving only the top-scoring tokens for downstream prediction. |
| Outcome: | The proposed framework outperforms CLIP by up to 11.19% in harmonic mean accuracy and achieves 0.8K additional FPS, with only 21 extra parameters. |
Rationalizing Text Matching: Learning Sparse Alignments via Optimal Transport (2020.acl-main)
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| Challenge: | Existing models that use only rationales to explain a prediction are limited by the complexity of deep neural networks. |
| Approach: | They extend selective rationalization to text matching by using optimal transport to find a minimal cost alignment between inputs. |
| Outcome: | The proposed model achieves very sparse rationale selections with high fidelity while preserving prediction accuracy compared to strong attention baseline models. |