Practical Solutions and Value of AI Research from Tenyx
Understanding Large Language Models (LLMs) and Their Reasoning Abilities
Large language models (LLMs) have shown impressive performance in various tasks, especially in reasoning. To enhance reasoning, techniques like chain of thought, retrieval augmented generation, and example-based prompting are used. However, these methods can lead to increased computational costs and inference latency.
Researchers at Tenyx explore the geometry of transformer layers in LLMs to understand their expressive power. They identify the density of token interactions and the relationship between model size and context length as critical factors for improved reasoning.
The study analyzes how the Multi-Layer Perceptron affects reasoning and demonstrates a correlation between expressive power and reasoning capabilities. It also reveals the impact of input complexity on improving LLMs’ reasoning performance.
The research highlights the importance of input space partitioning induced by MLPs in LLMs, showing how it impacts their function approximation abilities. The findings suggest that further exploration of these phenomena could enhance LLMs’ reasoning abilities.
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