Researchers from MIT and several other institutions have introduced an innovative technique that enhances the problem-solving capabilities of large language models by integrating programming and natural language. This new method, termed natural language embedded programs (NLEPs), significantly improves the accuracy and transparency of AI in tasks requiring numerical or symbolic reasoning.
Traditionally, large language models like those behind ChatGPT have excelled in tasks such as drafting documents, analysing sentiment, or translating languages. However, these models often struggle with tasks that demand numerical or symbolic reasoning. For instance, while a model might recite a list of U.S. presidents and their birthdays, it might falter when asked to identify which presidents elected after 1950 were born on a Wednesday. The solution to such problems lies beyond mere language processing.
MIT researchers propose a groundbreaking approach where the language model generates and executes a Python program to solve complex queries. NLEPs work by prompting the model to create a detailed program that processes the necessary data and then presents the solution in natural language. This method enhances the model’s ability to perform a wide range of reasoning tasks with higher accuracy.
How NLEPs Work
NLEPs follow a structured four-step process. First, the model identifies and calls the necessary functions to tackle the task. Next, it imports relevant natural language data required for the task, such as a list of presidents and their birthdays. In the
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