Preeti
The article discusses ways to make Large Language Models (LLMs) smarter in order to solve complex tasks. It covers various strategies such as Few Shot, Generated Knowledge, Chain of Thought (CoT), Self Reflection, Decomposed, Self Consistency, and ReAct. These strategies aim to enhance the reasoning capabilities of LLMs by breaking down tasks into smaller steps, incorporating external knowledge, and ensuring self-consistency in responses.
The article introduces the concepts of Chain-of-Thought (CoT) and Tree-of-Thought (ToT) as frameworks for guiding LLMs in their reasoning processes. CoT focuses on sequential logic, while ToT involves branching out to explore multiple sub-ideas. Additionally, the article discusses the Graph-of-Thoughts (GoT) approach, which uses a graph structure to represent LLM reasoning and allows for flexible reasoning and improved task handling.
Furthermore, the article introduces the Algorithm-of-Thoughts (AoT) framework, which decomposes problems into subproblems, generates solutions without pauses, explores branches using heuristics, backtracks to traverse promising paths, and emulates algorithmic search using LLM generation.
Overall, the article emphasizes the importance of enhancing LLM reasoning capabilities through structured frameworks like CoT, ToT, GoT, and AoT. These frameworks enable LLMs to tackle complex tasks more efficiently while maintaining the quality of their outputs. By mimicking algorithmic thinking and incorporating external knowledge, LLMs can become more adept at solving a wide range of tasks.
Source link
Source link: https://billtcheng2013.medium.com/large-language-model-reasoning-process-and-prompting-techniques-part-1-e3c31a78f1a0?source=rss——large_language_models-5
GIPHY App Key not set. Please check settings