‘Think-and-Execute’: A Machine Learning Framework that Encapsulates the Common Logical Structure of a Job Using Pseudocode for Efficient Reasoning in Large Language Models (LLMs)

In Large Language Models (LLMs), reasoning involves dissecting a problem’s logical structure and turning it into a sequence of logical steps that lead to a solution. For LLMs, this procedure has proven difficult, particularly in algorithmic reasoning where intricate logical patterns must be interpreted and transformed into a series of processes. Understanding patterns inside an […]

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