This AI Research from Tenyx Explore the Reasoning Abilities of Large Language Models (LLMs) Through Their Geometrical Understanding
What are the primary methods to enhance LLM reasoning?
The primary methods to enhance LLM reasoning include increasing model size, expanding context length, chain of thought, retrieval augmented generation, and example-based prompting. These approaches aim to improve the reasoning capabilities of large language models (LLMs) and have shown effectiveness in various tasks. However, they often lead to increased computational costs and inference latency in real-world applications.
What limitations exist in current LLM geometric analysis?
Current LLM geometric analysis has limitations such as lack of a comprehensive end-to-end geometric perspective, not accounting for the sequence dimension, and not offering context-dependent analysis, particularly in relation to model size, context length, and their roles in reasoning capabilities.
How do token interactions in MHA modules affect LLMs?
Token interactions in MHA modules play a crucial role in LLMs. The density of these interactions reflects the complexity of function representation achievable by the subsequent MLP layer3. Higher attention density, resulting from increased model size and context length, correlates with improved reasoning capabilities in LLMs3. This understanding may open avenues for enhancing LLM capabilities by increasing input complexity to MLP blocks.