Google Deepmind proposes ‘self-discover’ framework for LLMs, improves GPT-4 performance

Create an illustration that represents the concept of an article about the latest advances in language learning models (LLM) technology by Google Deepmind and the University of Southern California. Please include elements that signify self-discovery and improvement, such as a stylized LLM being enlightened by a figurative light bulb, symbolizing new-found knowledge and ability. Highlight actions of problem-solving and reasoning, possibly with a visual metaphor like a complex maze being easily navigated by the LLM. Design the image in a positive and light tone, comparable to the aesthetic found in early 20th-century graphic animation. Keep the aspect ratio as 3:2.

Researchers from Google Deepmind and the University of Southern California have proposed a new self-discover prompting framework to enhance the reasoning capabilities of large language models (LLMs). This approach goes beyond existing prompting techniques and has been found to improve the performance of models like OpenAI’s GPT-4 and Google’s PaLM 2. The framework involves LLMs self-discovering task-intrinsic reasoning structures to solve problems. It looks at multiple atomic reasoning modules and composes them into an explicit reasoning structure for LLMs to follow during decoding. This approach works with significantly less inference compute, making it beneficial for enterprises. The researchers tested the approach with various models and found notable performance improvements, with gains of up to 32% compared to other techniques. The self-discover approach achieved high accuracy across different reasoning tasks and showed potential for pushing the boundaries of problem-solving and advancing general intelligence in LLMs.

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