DTO: a Differentiable Training Objective for Effective Counterfactual Story Rewriting
Counterfactual story rewriting is a natural language processing task that requires updating an existing story to reflect a chosen alternative event, yet preserving all the unaffected storyline elements and overall coherence. While large language models have recently made remarkable progress on this task, it still remains challenging since the required modifications are typically very small in size and highly localized. As a consequence, models trained in a conventional manner with the maximum-likelihood training objective tend to overlook these nuances. At the same time, more sophisticated training approaches based on reinforcement learning are notoriously slow and difficult to set up. Fo...
| Authors | Amelia Girard, Massimo Piccardi |
|---|---|
| Primary category | cs.CL |
| Published | 24 May 2026 |
| Last updated at source | 24 May 2026 |
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