5 Strategies to Enhance LLM Reasoning

In this video, the host of code_your_own_AI explores five effective strategies to enhance the reasoning capabilities of large language models (LLMs) without fine-tuning them. The strategies include Chain-of-Thoughts, Tree-of-Thoughts, Graph-of-Thoughts, Abstraction-of-Thoughts, and a hybrid approach combining graph and abstraction methods. The video provides an overview of each method, highlighting their strengths and applications. Chain-of-Thoughts involves sequential reasoning, while Tree-of-Thoughts introduces branching paths and backtracking. Graph-of-Thoughts leverages a non-linear, multi-dimensional approach, closely resembling human neural structures. Abstraction-of-Thoughts focuses on high-level templates and solution frameworks. The host discusses recent research and benchmarks, comparing the performance of these methods using various LLMs, including Llama 3, Code Llama, and GPT-4. The video also delves into the potential of combining graph and abstraction methods for complex causal reasoning and planning in dynamic systems. The host emphasizes the importance of pre-training data quality and explores the challenges of integrating these methods into a unified system. The video concludes with a discussion on the future of AI research, particularly in multimodal and multitask learning, referencing recent advancements by Apple and Meta.