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Large language models (LLMs) have shown exceptional proficiency in natural language processing but often fall short of generating creative and original responses to open-ended questions.
Modes of thinking in young children: A study of the creativity-intelligence distinction
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Torrance Tests of Creative Thinking. Norms-Technical Manual. Research Edition. Verbal Tests Forms a and B. Figural Tests Forms a and B
Ellis Paul Torrance · 1966
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Groups: Interaction and performance
Joseph Edward McGrath · 1984
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Robert I Sutton and Andrew Hargadon · 1996
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Weiping Hu and Philip Adey · 2002
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Group creativity: Innovation through collaboration
Paul B Paulus and Bernard A Nijstad · 2003
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A report on the 40year follow-up of the torrance tests of creative thinking: Alive and well in the new millennium
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Scopes and methods of political science, 2011
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David kelley: From design to design thinking at stanford and ideo
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Language models are few-shot learners
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Evaluating large language models trained on code
Mark Chen, Jerry Tworek, Heewoo Jun, Qiming Yuan, Henrique Ponde, Jared Kaplan, and Harrison Edwards et al · 2021
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Training verifiers to solve math word problems
Karl Cobbe, Vineet Kosaraju, Mohammad Bavarian, Mark Chen, Heewoo Jun, Lukasz Kaiser, Matthias Plappert, Jerry Tworek, Jacob Hilton, Reiichiro Nakano, Christopher Hesse, and John Schulman · 2021
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Measuring massive multitask language understanding
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Is group work beneficial for producing creative designs in stem design education?
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Creative writing with an ai-powered writing assistant: Perspectives from professional writers
Daphne Ippolito, Ann Yuan, Andy Coenen, and Sehmon Burnam · 2022
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Large language models are zero-shot reasoners
Takeshi Kojima, Shixiang (Shane) Gu, Machel Reid, Yutaka Matsuo, and Yusuke Iwasawa · 2022
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Training language models to follow instructions with human feedback
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Putting gpt-3’s creativity to the (alternative uses) test
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Evaluating correctness and faithfulness of instruction-following models for question answering
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Art or artifice? large language models and the false promise of creativity
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Can large language models be an alternative to human evaluations?
Cheng-Han Chiang and Hung yi Lee · 2023
Is chatGPT a general-purpose natural language processing task solver?
Chengwei Qin, Aston Zhang, Zhuosheng Zhang, Jiaao Chen, Michihiro Yasunaga, and Diyi Yang · 2023
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Lamp: When large language models meet personalization
Alireza Salemi, Sheshera Mysore, Michael Bendersky, and Hamed Zamani · 2023
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Role play with large language models
Murray Shanahan, Kyle McDonell, and Laria Reynolds · 2023
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Character-llm: A trainable agent for role-playing
Yunfan Shao, Linyang Li, Junqi Dai, and Xipeng Qiu · 2023
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Brainstorm, then select: a generative language model improves its creativity score
Douglas Summers-Stay, Clare R. Voss, and Stephanie M. Lukin · 2023
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Corex: Pushing the boundaries of complex reasoning through multi-model collaboration
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Using gpt-4 to augment unbalanced data for automatic scoring
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The originality of machines: Ai takes the torrance test
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Multi-party chat: Conversational agents in group settings with humans and models
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Autogen: Enabling next-gen llm applications via multi-agent conversation
Qingyun Wu, Gagan Bansal, Jieyu Zhang, Yiran Wu, Beibin Li, Erkang Zhu, Li Jiang, Xiaoyun Zhang, Shaokun Zhang, Jiale Liu, Ahmed Hassan Awadallah, Ryen W White, Doug Burger, and Chi Wang · 2023
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Chateval: Towards better LLM-based evaluators through multi-agent debate
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