Natural language embedded programs (NLEPs) have been introduced to enhance the functionality of large language models. By generating Python code to address queries, NLEPs increase accuracy, efficiency, and transparency. This approach allows models to handle diverse tasks more effectively and could also benefit data privacy and smaller models. Credit: SciTechDaily.com
Researchers have developed a technique called natural language embedded programs (NLEPs) that improves the performance of large language models by generating Python programs to solve complex tasks.
This method not only enhances
These machine-learning models typically use only natural language to process information and answer queries, which can make it difficult for them to perform tasks that require numerical or symbolic reasoning.
For example, a large language model might be able to memorize and recite a list of recent U.S. presidents and their birthdays, but that same model could fail if asked the question “Which U.S. presidents elected after 1950 were born on a Wednesday?” (The answer is Jimmy Carter.)
A new technique enables large language models like GPT-4 to more accurately solve numeric or symbolic reasoning tasks by writing a Python program in code that generates the correct answer to a user’s query. Credit: Christine Daniloff, MIT; iStock
Enhancing Model Capabilities Through NLEPs
Researchers from SciTechDaily