Contextual Diversity Measure (CDM) for Controllable Story Generation in Large Language Models (2026.acl-srw)
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| Challenge: | Existing studies on controllable text generation focus on controlling attributes such as sentiment, writing style, and writing style. |
| Approach: | They introduce a metric that quantifies semantic diversity for scenario generation under fixed abstract semantic constraints and validate it through controlled experiments. |
| Outcome: | The proposed metric achieves excellent discrimination accuracy (100% and 91.9%, respectively), with discriminative power up to 5.5 greater than the best baseline. |
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| Challenge: | Existing studies on the use of Large Language Models (LLMs) focus primarily on English, leaving the cross-lingual generalization of aligned behavior underexplored. |
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Peng Xu, Mostofa Patwary, Mohammad Shoeybi, Raul Puri, Pascale Fung, Anima Anandkumar, Bryan Catanzaro
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| Challenge: | Existing approaches to enhance output diversity but compromise quality of outputs. |
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