AI-Augmented Brainwriting: Investigating the use of LLMs in group ideation

• URL: http://arxiv.org/abs/2402.14978v2
• Date: Thu, 29 Feb 2024 22:47:21 GMT
• Title: AI-Augmented Brainwriting: Investigating the use of LLMs in group ideation
• Authors: Orit Shaer, Angelora Cooper, Osnat Mokryn, Andrew L. Kun, Hagit Ben Shoshan
• Abstract summary: generative AI technologies such as large language models (LLMs) have significant implications for creative work. This paper explores two aspects of integrating LLMs into the creative process - the divergence stage of idea generation, and the convergence stage of evaluation and selection of ideas. We devised a collaborative group-AI Brainwriting ideation framework, which incorporated an LLM as an enhancement into the group ideation process.
• Score: 11.503226612030316
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The growing availability of generative AI technologies such as large language models (LLMs) has significant implications for creative work. This paper explores twofold aspects of integrating LLMs into the creative process - the divergence stage of idea generation, and the convergence stage of evaluation and selection of ideas. We devised a collaborative group-AI Brainwriting ideation framework, which incorporated an LLM as an enhancement into the group ideation process, and evaluated the idea generation process and the resulted solution space. To assess the potential of using LLMs in the idea evaluation process, we design an evaluation engine and compared it to idea ratings assigned by three expert and six novice evaluators. Our findings suggest that integrating LLM in Brainwriting could enhance both the ideation process and its outcome. We also provide evidence that LLMs can support idea evaluation. We conclude by discussing implications for HCI education and practice.

Related papers

• IdeaBench: Benchmarking Large Language Models for Research Idea Generation [19.66218274796796]
  Large Language Models (LLMs) have transformed how people interact with artificial intelligence (AI) systems. We propose IdeaBench, a benchmark system that includes a comprehensive dataset and an evaluation framework. Our dataset comprises titles and abstracts from a diverse range of influential papers, along with their referenced works. Our evaluation framework is a two-stage process: first, using GPT-4o to rank ideas based on user-specified quality indicators such as novelty and feasibility, enabling scalable personalization.
  arXiv  Detail & Related papers  (2024-10-31T17:04:59Z)
• Good Idea or Not, Representation of LLM Could Tell [86.36317971482755]
  We focus on idea assessment, which aims to leverage the knowledge of large language models to assess the merit of scientific ideas. We release a benchmark dataset from nearly four thousand manuscript papers with full texts, meticulously designed to train and evaluate the performance of different approaches to this task. Our findings suggest that the representations of large language models hold more potential in quantifying the value of ideas than their generative outputs.
  arXiv  Detail & Related papers  (2024-09-07T02:07:22Z)
• Can LLMs Generate Novel Research Ideas? A Large-Scale Human Study with 100+ NLP Researchers [90.26363107905344]
  Large language models (LLMs) have sparked optimism about their potential to accelerate scientific discovery. No evaluations have shown that LLM systems can take the very first step of producing novel, expert-level ideas.
  arXiv  Detail & Related papers  (2024-09-06T08:25:03Z)
• Cognitive LLMs: Towards Integrating Cognitive Architectures and Large Language Models for Manufacturing Decision-making [51.737762570776006]
  LLM-ACTR is a novel neuro-symbolic architecture that provides human-aligned and versatile decision-making. Our framework extracts and embeds knowledge of ACT-R's internal decision-making process as latent neural representations. Our experiments on novel Design for Manufacturing tasks show both improved task performance as well as improved grounded decision-making capability.
  arXiv  Detail & Related papers  (2024-08-17T11:49:53Z)
• Facilitating Holistic Evaluations with LLMs: Insights from Scenario-Based Experiments [0.22499166814992438]
  Even experienced faculty teams find it challenging to realize a holistic evaluation that accommodates diverse perspectives. This paper explores the use of a Large Language Model (LLM) as a facilitator to integrate diverse faculty assessments.
  arXiv  Detail & Related papers  (2024-05-28T01:07:06Z)
• Decompose and Aggregate: A Step-by-Step Interpretable Evaluation Framework [75.81096662788254]
  Large Language Models (LLMs) are scalable and economical evaluators. The question of how reliable these evaluators are has emerged as a crucial research question. We propose Decompose and Aggregate, which breaks down the evaluation process into different stages based on pedagogical practices.
  arXiv  Detail & Related papers  (2024-05-24T08:12:30Z)
• LLM Discussion: Enhancing the Creativity of Large Language Models via Discussion Framework and Role-Play [43.55248812883912]
  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. We propose LLM Discussion, a three-phase discussion framework that facilitates vigorous and diverging idea exchanges. We evaluate the efficacy of the proposed framework with the Alternative Uses Test, Similarities Test, Instances Test, and Scientific Creativity Test.
  arXiv  Detail & Related papers  (2024-05-10T10:19:14Z)
• FAC$^2$E: Better Understanding Large Language Model Capabilities by Dissociating Language and Cognition [56.76951887823882]
  Large language models (LLMs) are primarily evaluated by overall performance on various text understanding and generation tasks. We present FAC$2$E, a framework for Fine-grAined and Cognition-grounded LLMs' Capability Evaluation.
  arXiv  Detail & Related papers  (2024-02-29T21:05:37Z)
• Corex: Pushing the Boundaries of Complex Reasoning through Multi-Model Collaboration [83.4031923134958]
  Corex is a suite of novel general-purpose strategies that transform Large Language Models into autonomous agents. Inspired by human behaviors, Corex is constituted by diverse collaboration paradigms including Debate, Review, and Retrieve modes. We demonstrate that orchestrating multiple LLMs to work in concert yields substantially better performance compared to existing methods.
  arXiv  Detail & Related papers  (2023-09-30T07:11:39Z)
• "It Felt Like Having a Second Mind": Investigating Human-AI Co-creativity in Prewriting with Large Language Models [20.509651636971864]
  This study investigates human-LLM collaboration patterns and dynamics during prewriting. During collaborative prewriting, there appears to be a three-stage iterative Human-AI Co-creativity process.
  arXiv  Detail & Related papers  (2023-07-20T16:55:25Z)

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.