Mimetic Poet
- URL: http://arxiv.org/abs/2407.11984v1
- Date: Tue, 4 Jun 2024 02:50:15 GMT
- Title: Mimetic Poet
- Authors: Jon McCormack, Elliott Wilson, Nina Rajcic, Maria Teresa Llano,
- Abstract summary: This paper presents the design and initial assessment of a novel device that uses generative AI to facilitate creative ideation.
The device allows participants to compose short poetic texts by physically placing words on the device's surface.
Upon composing the text, the system employs a large language model (LLM) to generate a response, displayed on an e-ink screen.
- Score: 6.999740786886536
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: This paper presents the design and initial assessment of a novel device that uses generative AI to facilitate creative ideation, inspiration, and reflective thought. Inspired by magnetic poetry, which was originally designed to help overcome writer's block, the device allows participants to compose short poetic texts from a limited vocabulary by physically placing words on the device's surface. Upon composing the text, the system employs a large language model (LLM) to generate a response, displayed on an e-ink screen. We explored various strategies for internally sequencing prompts to foster creative thinking, including analogy, allegorical interpretations, and ideation. We installed the device in our research laboratory for two weeks and held a focus group at the conclusion to evaluate the design. The design choice to limit interactions with the LLM to poetic text, coupled with the tactile experience of assembling the poem, fostered a deeper and more enjoyable engagement with the LLM compared to traditional chatbot or screen-based interactions. This approach gives users the opportunity to reflect on the AI-generated responses in a manner conducive to creative thought.
Related papers
- Probing and Inducing Combinational Creativity in Vision-Language Models [52.76981145923602]
Recent advances in Vision-Language Models (VLMs) have sparked debate about whether their outputs reflect combinational creativity.
We propose the Identification-Explanation-Implication (IEI) framework, which decomposes creative processes into three levels.
To validate this framework, we curate CreativeMashup, a high-quality dataset of 666 artist-generated visual mashups annotated according to the IEI framework.
arXiv Detail & Related papers (2025-04-17T17:38:18Z) - Voice Interaction With Conversational AI Could Facilitate Thoughtful Reflection and Substantive Revision in Writing [23.89410974646694]
Writing well requires not only expressing ideas but also refining them through revision, a process facilitated by reflection.
Recent advancements in multi-modal large language models (LLMs) now offer new possibilities for supporting interactive and expressive voice-based reflection in writing.
We argue that voice-based interaction can naturally facilitate this conversational exchange, encouraging writers' engagement with higher-order concerns.
arXiv Detail & Related papers (2025-04-11T16:54:12Z) - Where is my Glass Slipper? AI, Poetry and Art [0.0]
This literature review interrogates the intersections between artificial intelligence, poetry, and art.
It traces the development of computer-generated poetry from early template-based systems to generative models.
Review calls for a re-evaluation of creative processes that recognises the interdependence of technological innovation and human subjectivity.
arXiv Detail & Related papers (2025-02-26T14:57:03Z) - Beyond Profile: From Surface-Level Facts to Deep Persona Simulation in LLMs [50.0874045899661]
We introduce CharacterBot, a model designed to replicate both the linguistic patterns and distinctive thought processes of a character.
Using Lu Xun as a case study, we propose four training tasks derived from his 17 essay collections.
These include a pre-training task focused on mastering external linguistic structures and knowledge, as well as three fine-tuning tasks.
We evaluate CharacterBot on three tasks for linguistic accuracy and opinion comprehension, demonstrating that it significantly outperforms the baselines on our adapted metrics.
arXiv Detail & Related papers (2025-02-18T16:11:54Z) - A Framework for Collaborating a Large Language Model Tool in Brainstorming for Triggering Creative Thoughts [2.709166684084394]
This study proposes a framework called GPS, which employs goals, prompts, and strategies to guide designers to systematically work with an LLM tool for improving the creativity of ideas generated during brainstorming.
Our framework, tested through a design example and a case study, demonstrates its effectiveness in stimulating creativity and its seamless LLM tool integration into design practices.
arXiv Detail & Related papers (2024-10-10T13:39:27Z) - AI as Humanity's Salieri: Quantifying Linguistic Creativity of Language Models via Systematic Attribution of Machine Text against Web Text [53.15652021126663]
We present CREATIVITY INDEX as the first step to quantify the linguistic creativity of a text.
To compute CREATIVITY INDEX efficiently, we introduce DJ SEARCH, a novel dynamic programming algorithm.
Experiments reveal that the CREATIVITY INDEX of professional human authors is on average 66.2% higher than that of LLMs.
arXiv Detail & Related papers (2024-10-05T18:55:01Z) - A Novel Idea Generation Tool using a Structured Conversational AI (CAI) System [0.0]
This paper presents a novel conversational AI-enabled active ideation interface as a creative idea-generation tool to assist novice designers.
It is a dynamic, interactive, and contextually responsive approach, actively involving a large language model (LLM) from the domain of natural language processing (NLP) in artificial intelligence (AI)
Integrating such AI models with ideation creates what we refer to as an Active Ideation scenario, which helps foster continuous dialogue-based interaction, context-sensitive conversation, and prolific idea generation.
arXiv Detail & Related papers (2024-09-09T16:02:27Z) - What if...?: Thinking Counterfactual Keywords Helps to Mitigate Hallucination in Large Multi-modal Models [50.97705264224828]
We propose Counterfactual Inception, a novel method that implants counterfactual thinking into Large Multi-modal Models.
We aim for the models to engage with and generate responses that span a wider contextual scene understanding.
Comprehensive analyses across various LMMs, including both open-source and proprietary models, corroborate that counterfactual thinking significantly reduces hallucination.
arXiv Detail & Related papers (2024-03-20T11:27:20Z) - Towards Full Authorship with AI: Supporting Revision with AI-Generated
Views [3.109675063162349]
Large language models (LLMs) are shaping a new user interface (UI) paradigm in writing tools by enabling users to generate text through prompts.
This paradigm shifts some creative control from the user to the system, thereby diminishing the user's authorship and autonomy in the writing process.
We introduce Textfocals, a prototype designed to investigate a human-centered approach that emphasizes the user's role in writing.
arXiv Detail & Related papers (2024-03-02T01:11:35Z) - Evaluating Large Language Model Creativity from a Literary Perspective [13.672268920902187]
This paper assesses the potential for large language models to serve as assistive tools in the creative writing process.
We develop interactive and multi-voice prompting strategies that interleave background descriptions, instructions that guide composition, samples of text in the target style, and critical discussion of the given samples.
arXiv Detail & Related papers (2023-11-30T16:46:25Z) - RELIC: Investigating Large Language Model Responses using Self-Consistency [58.63436505595177]
Large Language Models (LLMs) are notorious for blending fact with fiction and generating non-factual content, known as hallucinations.
We propose an interactive system that helps users gain insight into the reliability of the generated text.
arXiv Detail & Related papers (2023-11-28T14:55:52Z) - Explaining CLIP through Co-Creative Drawings and Interaction [0.0]
This paper analyses a visual archive of drawings produced by an interactive robotic art installation where audience members narrated their dreams into a system powered by CLIPdraw deep learning (DL) model.
The resulting archive of prompt-image pairs were examined and clustered based on concept representation accuracy.
arXiv Detail & Related papers (2023-06-12T21:15:25Z) - Channel-aware Decoupling Network for Multi-turn Dialogue Comprehension [81.47133615169203]
We propose compositional learning for holistic interaction across utterances beyond the sequential contextualization from PrLMs.
We employ domain-adaptive training strategies to help the model adapt to the dialogue domains.
Experimental results show that our method substantially boosts the strong PrLM baselines in four public benchmark datasets.
arXiv Detail & Related papers (2023-01-10T13:18:25Z) - Visualize Before You Write: Imagination-Guided Open-Ended Text
Generation [68.96699389728964]
We propose iNLG that uses machine-generated images to guide language models in open-ended text generation.
Experiments and analyses demonstrate the effectiveness of iNLG on open-ended text generation tasks.
arXiv Detail & Related papers (2022-10-07T18:01:09Z) - ImaginE: An Imagination-Based Automatic Evaluation Metric for Natural
Language Generation [53.56628907030751]
We propose ImaginE, an imagination-based automatic evaluation metric for natural language generation.
With the help of CLIP and DALL-E, two cross-modal models pre-trained on large-scale image-text pairs, we automatically generate an image as the embodied imagination for the text snippet.
Experiments spanning several text generation tasks demonstrate that adding imagination with our ImaginE displays great potential in introducing multi-modal information into NLG evaluation.
arXiv Detail & Related papers (2021-06-10T17:59:52Z)
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.