Solving Quantitative Reasoning Problems with Language Models ("Minerva") Breakthrough results on Math

NLP:

The field of computer science known as "Natural Language Processing" (NLP) is more particularly the field of "Artificial Intelligence" (AI) that is concerned with providing computers the capacity to comprehend written and spoken words in a manner similar to that of humans.
NLP blends statistical, machine learning, and deep learning models with computational linguistics—rule-based modelling of human language. With the use of these technologies, computers are now able to process human language in the form of text or audio data and fully "understand" what is being said or written, including the speaker's or writer's intentions and sentiment.

Minerva :

Minerva is a fast and flexible tool for deep learning. It provides NDarray programming interface, just like Numpy. Python bindings and C++ bindings are both available. The resulting code can be run on CPU or GPU. Multi-GPU support is very easy. Please refer to the examples to see how multi-GPU setting is used. Minerva is a fast and flexible tool for deep learning. It provides NDarray programming interface, just like Numpy. Python bindings and C++ bindings are both available. The resulting code can be run on CPU or GPU. Multi-GPU support is very easy. Please refer to the examples to see how multi-GPU setting is used.

Quantitative Reasoning :

One area where language models still perform well below that of humans is quantitative thinking. The ability to correctly parse a question with both natural language and mathematical notation, remember pertinent formulas and constants, and produce step-by-step solutions involving numerical calculations and symbolic manipulation are all necessary for solving mathematical and scientific problems. Due to these difficulties, it is frequently thought that using machine learning to solve quantitative reasoning problems will require significant improvements in model architecture and training methods, giving models access to external tools like Python interpreters, or even a more significant paradigm shift.

Solving Quantitative Reasoning Problems With Language Models (“Minerva”) :

In "Solving Quantitative Reasoning Problems With Language Models", we introduce Minerva, a language model that uses sequential reasoning to answer mathematical and scientific problems. We demonstrate that we significantly improve performance on a range of challenging quantitative reasoning tasks by concentrating on gathering training data that is pertinent for challenges in quantitative reasoning, training models at scale, and utilising best-in-class inference approaches. Without using external tools like a calculator, Minerva resolves such issues by providing solutions that incorporate numerical computations and symbolic manipulation. Using a combination of normal language and mathematical notation, the model parses and responds to mathematical queries. To attain cutting-edge performance on STEM reasoning tasks, Minerva uses a number of strategies, including few-shot prompting, chain of thought or scratchpad prompting, and majority voting

Example :

Conclusion :

The Minerva model excelled in testing, exceeding other cutting-edge models in resolving a variety of quantitative reasoning issues. These findings imply that language models may become more significant in disciplines like mathematics, physics, and engineering that demand complicated problem-solving abilities.
Overall, the Minerva model marks a substantial advance in the creation of language models, and its groundbreaking work in mathematics raises the possibility of the creation of further models of a similar nature.