Papers with creative generation
🧑‍🍳 Cooking Up Creativity: Enhancing LLM Creativity through Structured Recombination (2026.tacl-1)
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| Challenge: | Large Language Models excel at many tasks, yet struggle to generate truly creative ideas. |
| Approach: | They propose a novel approach that enhances Large Language Models' creativity by manipulating structured representations of existing ideas. |
| Outcome: | The proposed model outperforms GPT-4o in novelty and diversity and outperformed GPT-0 in creative generation. |
Beyond Noise: Characterizing Creative Potential in Unverifiable LLM Hallucinations (2026.acl-long)
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Yu Yan, Chunhong Zhang, Haiyu Zhao, Ziyang Zeng, Zihao Liu, Yongkang Wu, Jianzhou Diao, YiJie Chen, Shujie Wang, Zheng Hu
| Challenge: | Large Language Models generate outputs that extend beyond established knowledge . prior work does not characterize the unverifiable space as a whole . |
| Approach: | They propose a novelty-verifiability characterization that distinguishes Creative Synthesis from Groundless Fabrication by a conceptual creation task. |
| Outcome: | The proposed model distinguishes Creative Synthesis (Region A) from Groundless Fabrication (Regium B) it shows that Region A is non-negligible and robust, persisting across generation strategies, models, domains, and embedding choices. |
Towards A “Novel” Benchmark: Evaluating Literary Fiction with Large Language Models (2025.findings-acl)
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| Challenge: | Recent advances in Large Language Models (LLMs) context windows have enabled them to process inputs over 100K tokens and generate outputs of up to 10K token. |
| Approach: | They propose a multi-level evaluation framework that incorporates ten metrics across the Macro, Meso, and Micro levels and an annotated fiction dataset. |
| Outcome: | The proposed framework incorporates ten metrics across the Macro, Meso, and Micro levels and is based on a human-human-AI dataset. |
Diverse, not Short: A Length-Controlled Data Selection Strategy for Improving Response Diversity of Language Models (2025.emnlp-main)
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| Challenge: | Diverse language model responses are crucial for creative generation, open-ended tasks, and self-improvement training. |
| Approach: | They propose a length-controlled data selection strategy that improves diversity while maintaining length parity. |
| Outcome: | The proposed method improves diversity while maintaining length parity on LLaMA-3.1-8B and Olmo-2 family. |