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Roger E. Beaty

2 accepted papers

2025

Creative Preference Optimization

EMNLP 2025

While Large Language Models (LLMs) have demonstrated impressive performance across natural language generation tasks, their ability to generate truly creative content—characterized by novelty, diversity, surprise, and quality—remains limited. Existing methods for enhancing LLM creativity often focus

Cited by 0SourcePDFScholar
2025

Diverse, not Short: A Length-Controlled Data Selection Strategy for Improving Response Diversity of Language Models

EMNLP 2025

Diverse language model responses are crucial for creative generation, open-ended tasks, and self-improvement training. We show that common diversity metrics, and even reward models used for preference optimization, systematically bias models toward shorter outputs, limiting expressiveness. To addres

Cited by 0SourcePDFScholar