ORSO QGen: Odds-Ratio Steerable Optimization for Controlling Question Generation (2026.findings-eacl)
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| Challenge: | Existing methods for question generation rely on supervised fine-tuning . scarcity of datasets with multiple fine-grained attributes limits ability of models to generalize or transfer control to new combinations of attributes. |
| Approach: | They propose a framework to enhance attribute sensitivity in question generation models . they propose supervised fine-tuning with explicit attribute labels to produce questions that fit predefined characteristics. |
| Outcome: | The proposed framework improves attribute sensitivity while maintaining quality of output. |
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