The video details the creator’s efforts to develop a Thought Process Generator to enhance problem-solving in Generative Retrieval-Optimized Compression (GROC) models, focusing on solving complex multiplication problems. Despite initial challenges with accuracy and model responses, the creator iteratively refines their approach, including testing larger models, to improve performance and encourages viewer engagement with their findings.
In the video, the creator discusses the development of a Thought Process Generator aimed at enhancing problem-solving capabilities for Generative Retrieval-Optimized Compression (GROC) models, particularly in solving complex mathematical problems like high-digit multiplication. The initial step involves creating a script that generates a dataset of multiplication problems based on user-defined parameters, such as the number of digits and examples. The creator encounters issues with naming conflicts in Python, which they resolve by renaming their math module to avoid conflicts with the built-in math library.
After generating a dataset of five-digit multiplication problems, the creator tests the GROC model’s ability to solve these problems. They utilize the GROC API to evaluate the model’s performance, but the initial results show zero accuracy. The creator realizes that the model’s responses need to be parsed correctly, leading to adjustments in the script to remove commas from the answers for better matching. Despite these efforts, the model continues to struggle with accuracy, prompting the creator to explore the generation of a reasoning system message to improve performance.
The creator then attempts to generate a reasoning prompt using OpenAI’s models to guide the GROC model in solving multiplication problems. However, they face challenges with usage policy violations when trying to generate the system message. As a workaround, they switch to using a different GROC model, which still yields low accuracy results. The creator decides to iterate on the system message generation process, aiming to refine the instructions provided to the model to enhance its problem-solving capabilities.
As the creator continues testing, they reduce the complexity of the multiplication problems to four-digit by four-digit and then to three-digit by three-digit multiplications. They observe slight improvements in accuracy but still face limitations with the smaller GROC models. The creator emphasizes the importance of iterative improvement and feedback loops in refining the system message, ultimately leading to better performance in solving multiplication problems.
In the latter part of the video, the creator explores using larger models, such as the 70 billion parameter version of Llama, to see if they can achieve better results. They find that larger models tend to handle detailed system prompts more effectively, leading to improved accuracy in solving multiplication problems. The creator concludes by discussing the potential benefits of their approach and the importance of sharing their findings and code with patrons, encouraging viewers to engage with their content for further learning and development.