Unleashing Creativity in Large Language Models: Exploring Leap-of-Thought for Innovative Humor Generation

Основные понятия

Large language models can be empowered with leap-of-thought abilities to generate creative and unexpected humor responses by associating seemingly unrelated concepts.

Аннотация

This paper explores the Leap-of-Thought (LoT) abilities within large language models (LLMs) - a non-sequential, creative paradigm involving strong associations and knowledge leaps. The authors first build a multimodal and multilingual Oogiri-GO dataset, which contains over 130,000 samples from the Oogiri game, a traditional Japanese creative game that challenges participants to provide unexpected and humorous responses to prompts in the form of images, text, or a combination of both.

Through extensive experiments, the authors find that even advanced LLMs and reasoning frameworks struggle to exhibit sufficient LoT ability for creative humor generation. To address this, they propose the Creative Leap-of-Thought (CLoT) paradigm, which consists of two stages:

1. Associable Instruction Tuning: This stage formulates the Oogiri-GO dataset into LoT-oriented instruction data to train the LLM to improve its LoT-based humor generation and discrimination abilities.

2. Explorative Self-Refinement: This stage encourages the LLM to generate more creative LoT data by exploring parallels between seemingly unrelated concepts and selects high-quality data to train itself for self-refinement.

The experimental results show that CLoT can greatly enhance the LoT ability of LLMs like Qwen and CogVLM across several types of Oogiri games. CLoT-integrated LLMs also achieve higher quantitative performance than the corresponding vanilla and CoT-integrated LLMs. Additionally, CLoT can boost creative abilities on other tasks like "cloud guessing game" and "divergent association task", demonstrating its remarkable generalization ability.

These findings advance the understanding of LoT in LLMs and offer a pathway to improve their creative capacities for innovative applications across domains.

Статистика

"Completing a paper is actually very enjoyable."
"Not for me, but it's hard for my supervisor."
"What else can wake you up besides coffee when you are coding?"
"Maybe you need to enlist the help of some angry bees."
"A cup of deadline."

Цитаты

"Chain-of-Thought (CoT) [2, 3] guides large language models (LLMs) to reason step-by-step, and can motivate their logical reasoning ability."
"While effective for logical tasks, CoT is not conducive to creative problem-solving which often requires out-of-box thoughts and is crucial for innovation advancements."
"Leap-of-Thought (LoT) [19, 20], a.k.a. mental leap [21–24]—the art of non-sequential thinking by association, drawing parallels between seemingly unrelated concepts, and facilitating a "leap" of knowledge transfer."

Ключевые выводы из

Let's Think Outside the Box: Exploring Leap-of-Thought in Large Language Models with Creative Humor Generation

by Shanshan Zho... в arxiv.org 04-23-2024

https://arxiv.org/pdf/2312.02439.pdf

Дополнительные вопросы

How can the CLoT paradigm be further extended to other creative tasks beyond humor generation?

The CLoT paradigm can be extended to other creative tasks beyond humor generation by adapting the framework to suit the specific requirements of different tasks. One way to do this is by customizing the instruction templates and the explorative self-refinement process to align with the unique characteristics of the new creative tasks. For example, for tasks that involve image recognition or creative writing, the instruction templates can be tailored to include relevant prompts and conditions that stimulate creative thinking in those domains. Additionally, the explorative self-refinement stage can be modified to encourage the generation of diverse and innovative responses specific to the new tasks. By adapting the CLoT framework to different creative tasks, LLMs can be trained to excel in a variety of creative applications beyond humor generation.

What are the potential limitations of the CLoT approach, and how can they be addressed in future research?

One potential limitation of the CLoT approach could be the scalability and generalization of the framework across a wide range of creative tasks. As the complexity and diversity of creative tasks increase, there may be challenges in effectively training LLMs to exhibit strong LoT abilities in all scenarios. To address this limitation, future research could focus on developing more sophisticated instruction templates and explorative self-refinement strategies that are adaptable to different creative tasks. Additionally, incorporating feedback mechanisms and reinforcement learning techniques could help enhance the learning process and improve the LoT abilities of LLMs over time. Furthermore, exploring the transferability of the CLoT framework to new domains and tasks through extensive experimentation and evaluation can help identify and overcome potential limitations.

How can the exploration of parallels between seemingly unrelated concepts be better facilitated to enhance the LoT abilities of LLMs?

To enhance the exploration of parallels between seemingly unrelated concepts and improve the LoT abilities of LLMs, several strategies can be implemented. Firstly, incorporating a more diverse and extensive set of weakly-associated conditions during the explorative remote association stage can stimulate the LLM to make unique connections and foster creative thinking. By expanding the range of conditions and encouraging the LLM to explore unconventional associations, the framework can facilitate the generation of more innovative and unexpected responses. Additionally, introducing mechanisms for adaptive learning and dynamic adjustment of exploration parameters based on the LLM's performance can help fine-tune the exploration process and optimize the generation of creative outputs. Moreover, leveraging advanced techniques such as meta-learning and continual learning can enable the LLM to adapt and improve its LoT abilities over time, leading to enhanced creativity and problem-solving skills.