Papers by Dennis Assenmacher
People Make Better Edits: Measuring the Efficacy of LLM-Generated Counterfactually Augmented Data for Harmful Language Detection (2023.emnlp-main)
Copied to clipboard
| Challenge: | Past work has shown that counterfactually augmented data (CADs) can improve models' performance on out-of-domain tests. |
| Approach: | They use Polyjuice, ChatGPT, and Flan-T5 to automatically generate CADs . they find that CAD generates a model that flips the original label with minimal changes . |
| Outcome: | The proposed model improves model robustness on out-of-domain test sets and individual data points. |