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| Autori principali: | , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2412.03151 |
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| _version_ | 1866910958267400192 |
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| author | Sun, Luning Yuan, Yuzhuo Yao, Yuan Li, Yanyan Zhang, Hao Xie, Xing Wang, Xiting Luo, Fang Stillwell, David |
| author_facet | Sun, Luning Yuan, Yuzhuo Yao, Yuan Li, Yanyan Zhang, Hao Xie, Xing Wang, Xiting Luo, Fang Stillwell, David |
| contents | Artificial intelligence has, so far, largely automated routine tasks, but what does it mean for the future of work if Large Language Models (LLMs) show creativity comparable to humans? To measure the creativity of LLMs holistically, the current study uses 13 creative tasks spanning three domains. We benchmark the LLMs against individual humans, and also take a novel approach by comparing them to the collective creativity of groups of humans. We find that the best LLMs (Claude and GPT-4) rank in the 52nd percentile against humans, and overall LLMs excel in divergent thinking and problem solving but lag in creative writing. When questioned 10 times, an LLM's collective creativity is equivalent to 8-10 humans. When more responses are requested, two additional responses of LLMs equal one extra human. Ultimately, LLMs, when optimally applied, may compete with a small group of humans in the future of work. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_03151 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Large Language Models show both individual and collective creativity comparable to humans Sun, Luning Yuan, Yuzhuo Yao, Yuan Li, Yanyan Zhang, Hao Xie, Xing Wang, Xiting Luo, Fang Stillwell, David Artificial Intelligence Artificial intelligence has, so far, largely automated routine tasks, but what does it mean for the future of work if Large Language Models (LLMs) show creativity comparable to humans? To measure the creativity of LLMs holistically, the current study uses 13 creative tasks spanning three domains. We benchmark the LLMs against individual humans, and also take a novel approach by comparing them to the collective creativity of groups of humans. We find that the best LLMs (Claude and GPT-4) rank in the 52nd percentile against humans, and overall LLMs excel in divergent thinking and problem solving but lag in creative writing. When questioned 10 times, an LLM's collective creativity is equivalent to 8-10 humans. When more responses are requested, two additional responses of LLMs equal one extra human. Ultimately, LLMs, when optimally applied, may compete with a small group of humans in the future of work. |
| title | Large Language Models show both individual and collective creativity comparable to humans |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2412.03151 |