Papers with Training models
ESCAPE: a Large-scale Synthetic Corpus for Automatic Post-Editing (L18-1)
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| Challenge: | eSCAPE is the largest freely-available Synthetic Corpus for Automatic Post-Editing released so far. |
| Approach: | a team of researchers develops a Synthetic Corpus for Automatic Post-Editing . eSCAPE is the largest freely-available Synthetic corpus for automatic post-editing released so far . the results prove that the models always improve MT quality with statistically significant gains . |
| Outcome: | eSCAPE is the largest freely-available Synthetic Corpus for Automatic Post-Editing released so far. |
Value-based Search in Execution Space for Mapping Instructions to Programs (N19-1)
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| Challenge: | Existing methods to map instructions to programs require searching for good programs at training time. |
| Approach: | They propose a search algorithm that uses the target world state to train a critic network that predicts the expected reward of every search state. |
| Outcome: | The proposed algorithm significantly improves on all three domains compared to baselines on the SCONE dataset. |
DP-FROST: Differentially Private Fine-tuning of Pre-trained Models with Freezing Model Parameters (2025.coling-main)
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| Challenge: | Training models with differential privacy has received a lot of attention since it provides theoretical guarantee of privacy preservation. |
| Approach: | They propose methods that fine-tune large-scale pre-trained models with freezing unimportant parameters for downstream tasks while satisfying differential privacy. |
| Outcome: | The proposed methods fine-tune large pre-trained models with freezing unimportant parameters while satisfying differential privacy while preserving their utility. |
CoLA: A Choice Leakage Attack Framework to Expose Privacy Risks in Subset Training (2026.acl-long)
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| Challenge: | Existing threat models underestimate subset-training privacy risks because of the scale of modern datasets. |
| Approach: | They propose a unified framework for analyzing privacy leakage in subset selection based on side-channel metadata from the subset process or via the outputs of the target model. |
| Outcome: | The proposed framework analyzes privacy leakage in subset selection based on two different scenarios . |