Characterising the Creative Process in Humans and Large Language Models

• URL: http://arxiv.org/abs/2405.00899v2
• Date: Wed, 5 Jun 2024 19:15:43 GMT
• Title: Characterising the Creative Process in Humans and Large Language Models
• Authors: Surabhi S. Nath, Peter Dayan, Claire Stevenson,
• Abstract summary: We provide an automated method to characterise how humans and LLMs explore semantic spaces on the Alternate Uses Task. We use sentence embeddings to identify response categories and compute semantic similarities, which we use to generate jump profiles. Our results corroborate earlier work in humans reporting both persistent (deep search in few semantic spaces) and flexible (broad search across multiple semantic spaces) pathways to creativity. Though LLMs as a population match human profiles, their relationship with creativity is different, where the more flexible models score higher on creativity.
• Score: 6.363158395541767
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large language models appear quite creative, often performing on par with the average human on creative tasks. However, research on LLM creativity has focused solely on \textit{products}, with little attention on the creative \textit{process}. Process analyses of human creativity often require hand-coded categories or exploit response times, which do not apply to LLMs. We provide an automated method to characterise how humans and LLMs explore semantic spaces on the Alternate Uses Task, and contrast with behaviour in a Verbal Fluency Task. We use sentence embeddings to identify response categories and compute semantic similarities, which we use to generate jump profiles. Our results corroborate earlier work in humans reporting both persistent (deep search in few semantic spaces) and flexible (broad search across multiple semantic spaces) pathways to creativity, where both pathways lead to similar creativity scores. LLMs were found to be biased towards either persistent or flexible paths, that varied across tasks. Though LLMs as a population match human profiles, their relationship with creativity is different, where the more flexible models score higher on creativity. Our dataset and scripts are available on \href{https://github.com/surabhisnath/Creative_Process}{GitHub}.

Related papers

• Evaluating Creative Short Story Generation in Humans and Large Language Models [0.7965327033045846]
  Large language models (LLMs) have recently demonstrated the ability to generate high-quality stories. We conduct a systematic analysis of creativity in short story generation across LLMs and everyday people. Our findings reveal that while LLMs can generate stylistically complex stories, they tend to fall short in terms of creativity when compared to average human writers.
  arXiv  Detail & Related papers  (2024-11-04T17:40:39Z)
• Unified Generative and Discriminative Training for Multi-modal Large Language Models [88.84491005030316]
  Generative training has enabled Vision-Language Models (VLMs) to tackle various complex tasks. Discriminative training, exemplified by models like CLIP, excels in zero-shot image-text classification and retrieval. This paper proposes a unified approach that integrates the strengths of both paradigms.
  arXiv  Detail & Related papers  (2024-11-01T01:51:31Z)
• Systematic Task Exploration with LLMs: A Study in Citation Text Generation [63.50597360948099]
  Large language models (LLMs) bring unprecedented flexibility in defining and executing complex, creative natural language generation (NLG) tasks. We propose a three-component research framework that consists of systematic input manipulation, reference data, and output measurement. We use this framework to explore citation text generation -- a popular scholarly NLP task that lacks consensus on the task definition and evaluation metric.
  arXiv  Detail & Related papers  (2024-07-04T16:41:08Z)
• HoLLMwood: Unleashing the Creativity of Large Language Models in Screenwriting via Role Playing [45.95600225239927]
  Large language models (LLMs) can hardly produce written works at the level of human experts due to the extremely high complexity of literature writing. We present HoLLMwood, an automated framework for unleashing the creativity of LLMs and exploring their potential in screenwriting.
  arXiv  Detail & Related papers  (2024-06-17T16:01:33Z)
• Creativity Has Left the Chat: The Price of Debiasing Language Models [1.223779595809275]
  We investigate the unintended consequences of Reinforcement Learning from Human Feedback on the creativity of Large Language Models (LLMs) Our findings have significant implications for marketers who rely on LLMs for creative tasks such as copywriting, ad creation, and customer persona generation.
  arXiv  Detail & Related papers  (2024-06-08T22:14:51Z)
• Divergent Creativity in Humans and Large Language Models [37.67363469600804]
  The recent surge in the capabilities of Large Language Models has led to claims that they are approaching a level of creativity akin to human capabilities. We leverage recent advances in creativity science to build a framework for in-depth analysis of divergent creativity in both state-of-the-art LLMs and a substantial dataset of 100,000 humans.
  arXiv  Detail & Related papers  (2024-05-13T22:37:52Z)
• LMRL Gym: Benchmarks for Multi-Turn Reinforcement Learning with Language Models [56.25156596019168]
  This paper introduces the LMRL-Gym benchmark for evaluating multi-turn RL for large language models (LLMs) Our benchmark consists of 8 different language tasks, which require multiple rounds of language interaction and cover a range of tasks in open-ended dialogue and text games.
  arXiv  Detail & Related papers  (2023-11-30T03:59:31Z)
• AlignedCoT: Prompting Large Language Models via Native-Speaking Demonstrations [52.43593893122206]
  Alignedcot is an in-context learning technique for invoking Large Language Models. It achieves consistent and correct step-wise prompts in zero-shot scenarios. We conduct experiments on mathematical reasoning and commonsense reasoning.
  arXiv  Detail & Related papers  (2023-11-22T17:24:21Z)
• Eliciting Human Preferences with Language Models [56.68637202313052]
  Language models (LMs) can be directed to perform target tasks by using labeled examples or natural language prompts. We propose to use *LMs themselves* to guide the task specification process. We study GATE in three domains: email validation, content recommendation, and moral reasoning.
  arXiv  Detail & Related papers  (2023-10-17T21:11:21Z)
• A Mathematical Abstraction for Balancing the Trade-off Between Creativity and Reality in Large Language Models [35.25919932657958]
  Large Language Models are increasingly being used in domains such as generating prose, poetry or art. This work provides a mathematical abstraction to describe creativity and reality based on certain losses. A model trained on these losses balances the trade-off between the creativity and reality of the model.
  arXiv  Detail & Related papers  (2023-06-04T08:12:34Z)
• Check Your Facts and Try Again: Improving Large Language Models with External Knowledge and Automated Feedback [127.75419038610455]
  Large language models (LLMs) are able to generate human-like, fluent responses for many downstream tasks. This paper proposes a LLM-Augmenter system, which augments a black-box LLM with a set of plug-and-play modules.
  arXiv  Detail & Related papers  (2023-02-24T18:48:43Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.