Big language models are the new trend for very good reasons. These models use deep learning techniques and are trained on large amounts of textual data. They produce human-like text and perform various Natural Language Processing (NLP) and Natural Language Understanding (NLU) tasks. Some famous LLMs like GPT 3.5, GPT 4, BERT, DALL-E, and T5 perform various tasks like generating meaningful answers to questions, text summaries, translations, text-to-text transformation, etc.
Recently, a new approach called dreamGPT was introduced, which uses the hallucinatory power of large language models to stimulate divergent thinking. This innovative approach helps generate unique and creative ideas. Whereas, on the one hand, where hallucinations are typically associated with a negative connotation and mainly mentioned as a drawback of LLMs, DreamGPT enables the transformation of hallucinations into something valuable to generate innovative solutions.
Today’s LLMs are primarily designed to address particular problems in understanding and generating text based on instructions or prompts. But these models are limited to generating responses that align with the existing patterns they have learned from the data they have been trained on. This restricts your ability to explore alternative or unconventional ideas. Here comes DreamGPT, with a different methodology to make use of the inherent ability of LLMs to hallucinate. During text generation, the goal of this approach is the production of a text that may not have a direct basis in reality but can still be useful and creative.
This can help dreamGPT to explore different use cases and use divergent thinking. Divergent thinking refers to coming up with a wide range of creative ideas, considering multiple perspectives, and exploring different solutions. With this, DreamGPT can explore as many possibilities as possible instead of just aiming for a single correct answer or a specific problem solving approach.
To use dreamGPT, users must install Python 3.10+ and Poetry. Poetry is a tool used for dependency management and packaging in Python. It allows the declaration of the libraries used in a project and helps to install and update them. DreamGPT works in a loop planting seeds at random, dreaming up new and creative ideas, combining and evaluating different approaches, selecting the most novel approach, and repeating it in a loop.
dreamGPT is open source in nature and can run locally on any PC or Mac without the need for a GPU on the device. Examples have been shown in the Github readme, which can be accessed at here. When running DreamGPT, it generates a random seed of concepts and uses them as the starting point for its dreaming process. Each idea is evaluated based on various criteria and the score is used to reward the best ideas over time. With population growth, results improve.
In conclusion, dreamGPT is a great approach that embraces the hallucinatory abilities of LLMs and shows promise for stimulating divergent thinking and generating innovative ideas.
review the GitHub and Reddit post. Don’t forget to join our 21k+ ML SubReddit, discord channel, and electronic newsletter, where we share the latest AI research news, exciting AI projects, and more. If you have any questions about the article above or if we missed anything, feel free to email us at [email protected]
🚀 Check out 100 AI tools at AI Tools Club
Tanya Malhotra is a final year student at the University of Petroleum and Power Studies, Dehradun, studying BTech in Computer Engineering with a specialization in Artificial Intelligence and Machine Learning.
She is a data science enthusiast with good analytical and critical thinking, along with a keen interest in acquiring new skills, leading groups, and managing work in an organized manner.
