# A Survey of Question Answering for Math and Science Problem

arXiv: https://arxiv.org/abs/1705.04530 · HTML: https://mlsanity.com/paper/1705.04530/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 May 2017.

## Built on by (most cited first, 4 of 4)

- [WizardMath: Empowering Mathematical Reasoning for Large Language Models via Reinforced Evol-Instruct](https://mlsanity.com/paper/2308.09583/) (2023)
- [A Survey of Deep Learning for Mathematical Reasoning](https://mlsanity.com/paper/2212.10535/) (2022)
- [AutoPRM: Automating Procedural Supervision for Multi-Step Reasoning via Controllable Question Decomposition](https://mlsanity.com/paper/2402.11452/) (2024)
- [No Train Still Gain. Unleash Mathematical Reasoning of Large Language Models with Monte Carlo Tree Search Guided by Energy Function](https://mlsanity.com/paper/2309.03224/) (2023)

## Abstract (arXiv)

Turing test was long considered the measure for artificial intelligence. But with the advances in AI, it has proved to be insufficient measure. We can now aim to mea- sure machine intelligence like we measure human intelligence. One of the widely accepted measure of intelligence is standardized math and science test. In this paper, we explore the progress we have made towards the goal of making a machine smart enough to pass the standardized test. We see the challenges and opportunities posed by the domain, and note that we are quite some ways from actually making a system as smart as a even a middle school scholar.