Google Research's Minerva project has made significant strides in solving quantitative reasoning problems using language models, showcasing substantial performance improvements in mathematical and scientific tasks. Minerva operates by parsing and processing questions that include mathematical notation and generating step-by-step solutions involving numerical calculations and symbolic manipulation, all without the need for external tools like calculators. Employing techniques such as few-shot prompting, chain of thought prompting, and majority voting, Minerva has achieved state-of-the-art performance on a variety of STEM reasoning tasks. Through its advanced prompting and evaluation methods, Minerva has become an indispensable tool for exploring complex quantitative problems, offering great potential in scientific research and educational applications.

Top Features:
  1. PaLM-based Model: Builds on Google's Pathways Language Model with specialized training.

  2. Advanced Techniques: Employs few-shot prompting, chain of thought prompting, and majority voting for problem-solving.

  3. State-of-the-art Performance: Achieves leading results on STEM benchmarks.

  4. Interactive Sample Explorer: Allows users to investigate Minerva’s problem-solving process.

  5. Wide Application Scope: Useful for scientific research and education, capable of aiding researchers, and enabling new learning opportunities.


1) What is Minerva?

Minerva is a language model developed by Google Research capable of solving mathematical and scientific questions using step-by-step reasoning.

2) How does Minerva solve quantitative reasoning problems differently from other models?

Minerva differs by generating solutions that include numerical calculations and symbolic manipulation without relying on external tools such as calculators.

3) What techniques does Minerva use to achieve its performance?

The Minerva language model incorporates few-shot prompting, chain of thought or scratchpad prompting, and majority voting as part of its technique to solve problems.

4) How well does Minerva perform on STEM benchmarks?

Minerva's performance was tested on various STEM benchmarks and has obtained state-of-the-art results in many cases, performing well in problem sets from grade school to graduate level.

5) What are some limitations of Minerva?

While Minerva handles a diverse set of problems, its answers are not automatically verifiable like formal mathematical methods, and it might produce correct answers with incorrect reasoning which cannot be automatically detected.




Google Research Minerva Quantitative Reasoning Language Models STEM PaLM


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