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A Summary of 'Creative Problem Solving in Large Language and Vision Models – What Would it Take?' by Georgia Institute of Technology & Tufts
A Summary of Georgia Institute of Technology & Tufts University, Medford's 'Creative Problem Solving in Large Language and Vision Models – What Would it Take?' Available at: https://arxiv.org/abs/2405.01453 This summary is AI generated, however the creators of the AI that produces this summary have made every effort to ensure that it is of high quality. As AI systems can be prone to hallucinations we always recommend readers seek out and read the original source material. Our intention is to help listeners save time and stay on top of trends and new discoveries. You can find the introductory section of this recording provided below... This is a summary of the research paper titled "Creative Problem Solving in Large Language and Vision Models – What Would it Take?" The contributing authors are from the Georgia Institute of Technology and Tufts University, Medford. The paper was published on May 2, 2024. In this publication, the authors explore the integration of Computational Creativity (CC) with research in large language and vision models (LLVMs). They aim to address a significant limitation of these models, which is creative problem solving. Through preliminary experiments, the authors show how principles of CC can be applied to LLVMs through augmented prompting. This approach seeks to enhance the models' ability to solve problems creatively, which has been a notable shortcoming, particularly when compared to human capabilities in similar tasks. The paper begins by defining creativity and its importance in the field of artificial intelligence. It specifies that creative problem solving in LLVMs is an aspect of creativity that focuses on discovering novel ways to accomplish tasks. The authors highlight the current gap in the capability of state-of-art LLVMs, such as GPT-4, which struggle with tasks that require 'Eureka' ideas or creative solutions. The research aims to foster discussions on integrating machine learning and computational creativity to bridge this gap, enhancing the creative problem-solving abilities of LLVMs. Margaret A. Boden's seminal work on three forms of creativity—exploratory, combinational, and transformational—is discussed as a framework to apply to LLVMs. The authors propose that LLVMs can be improved by focusing not only on 'search' strategies but also on these creative approaches to problem-solving. The paper also explores how typical task planning with LLVMs is executed, distinguishing between high-level, low-level, and hybrid task planning methods. Each method provides insight into how LLVMs can be adjusted to incorporate creative problem-solving capabilities. An overview of how embedding spaces in LLVMs can be augmented for creative problem solving is also presented. This involves adapting the models' 'way of thinking' to interpret and generate novel solutions to problems. In summary, the paper calls for a closer integration of machine learning and computational creativity to address the limitations of LLVMs in creative problem solving. By applying principles from computational creativity, the authors aim to enhance the ingenuity of LLVMs in problem-solving contexts, especially those requiring innovative approaches due to resource constraints or novel challenges.
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By James Bentley
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A Summary of Georgia Institute of Technology & Tufts University, Medford's 'Creative Problem Solving in Large Language and Vision Models – What Would it Take?' Available at: https://arxiv.org/abs/2405.01453 This summary is AI generated, however the creators of the AI that produces this summary have made every effort to ensure that it is of high quality. As AI systems can be prone to hallucinations we always recommend readers seek out and read the original source material. Our intention is to help listeners save time and stay on top of trends and new discoveries. You can find the introductory section of this recording provided below... This is a summary of the research paper titled "Creative Problem Solving in Large Language and Vision Models – What Would it Take?" The contributing authors are from the Georgia Institute of Technology and Tufts University, Medford. The paper was published on May 2, 2024. In this publication, the authors explore the integration of Computational Creativity (CC) with research in large language and vision models (LLVMs). They aim to address a significant limitation of these models, which is creative problem solving. Through preliminary experiments, the authors show how principles of CC can be applied to LLVMs through augmented prompting. This approach seeks to enhance the models' ability to solve problems creatively, which has been a notable shortcoming, particularly when compared to human capabilities in similar tasks. The paper begins by defining creativity and its importance in the field of artificial intelligence. It specifies that creative problem solving in LLVMs is an aspect of creativity that focuses on discovering novel ways to accomplish tasks. The authors highlight the current gap in the capability of state-of-art LLVMs, such as GPT-4, which struggle with tasks that require 'Eureka' ideas or creative solutions. The research aims to foster discussions on integrating machine learning and computational creativity to bridge this gap, enhancing the creative problem-solving abilities of LLVMs. Margaret A. Boden's seminal work on three forms of creativity—exploratory, combinational, and transformational—is discussed as a framework to apply to LLVMs. The authors propose that LLVMs can be improved by focusing not only on 'search' strategies but also on these creative approaches to problem-solving. The paper also explores how typical task planning with LLVMs is executed, distinguishing between high-level, low-level, and hybrid task planning methods. Each method provides insight into how LLVMs can be adjusted to incorporate creative problem-solving capabilities. An overview of how embedding spaces in LLVMs can be augmented for creative problem solving is also presented. This involves adapting the models' 'way of thinking' to interpret and generate novel solutions to problems. In summary, the paper calls for a closer integration of machine learning and computational creativity to address the limitations of LLVMs in creative problem solving. By applying principles from computational creativity, the authors aim to enhance the ingenuity of LLVMs in problem-solving contexts, especially those requiring innovative approaches due to resource constraints or novel challenges.