# Problem-Solving Guide: Predicting the Algorithm Tags and Difficulty for Competitive Programming Problems

arXiv: https://arxiv.org/abs/2310.05791 · HTML: https://mlsanity.com/paper/2310.05791/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 36 references that are in the map.
- First posted on arXiv 9 Oct 2023.

## Builds on (most cited first, 15 of 15)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://mlsanity.com/paper/1905.11946/) (2019)
- [Longformer: The Long-Document Transformer](https://mlsanity.com/paper/2004.05150/) (2020)
- [Big Bird: Transformers for Longer Sequences](https://mlsanity.com/paper/2007.14062/) (2020)
- [Gradient Surgery for Multi-Task Learning](https://mlsanity.com/paper/2001.06782/) (2020)
- [Convolutional Neural Networks for Sentence Classification](https://mlsanity.com/paper/1408.5882/) (2014)
- [Bag of Tricks for Efficient Text Classification](https://mlsanity.com/paper/1607.01759/) (2016)
- [An Overview of Multi-Task Learning in Deep Neural Networks](https://mlsanity.com/paper/1706.05098/) (2017)
- [Multi-Task Learning with Deep Neural Networks: A Survey](https://mlsanity.com/paper/2009.09796/) (2020)
- [Pareto Multi-Task Learning](https://mlsanity.com/paper/1912.12854/) (2019)
- [Wider or Deeper: Revisiting the ResNet Model for Visual Recognition](https://mlsanity.com/paper/1611.10080/) (2016)
- [Text Classification Algorithms: A Survey](https://mlsanity.com/paper/1904.08067/) (2019)
- [Large-scale Multi-label Text Classification - Revisiting Neural Networks](https://mlsanity.com/paper/1312.5419/) (2013)
- [Predicting Algorithm Classes for Programming Word Problems](https://mlsanity.com/paper/1903.00830/) (2019)

## Built on by (most cited first, 5 of 5)

- [Exploring Large Language Models for Translating Romanian Computational Problems into English](https://mlsanity.com/paper/2501.05601/) (2025)
- [A New Korean Text Classification Benchmark for Recognizing the Political Intents in Online Newspapers](https://mlsanity.com/paper/2311.01712/) (2023)
- [CogTax: A Four-Level Cognitive Taxonomy for Command-Line Computing Education](https://mlsanity.com/paper/2607.00140/) (2026)
- [Evaluating the Performance of Large Language Models in Competitive Programming: A Multi-Year, Multi-Grade Analysis](https://mlsanity.com/paper/2409.09054/) (2024)
- [MAS-Algorithm: A Workflow for Solving Algorithmic Programming Problems with a Multi-Agent System](https://mlsanity.com/paper/2605.05949/) (2026)

## Abstract (arXiv)

The recent program development industries have required problem-solving abilities for engineers, especially application developers. However, AI-based education systems to help solve computer algorithm problems have not yet attracted attention, while most big tech companies require the ability to solve algorithm problems including Google, Meta, and Amazon. The most useful guide to solving algorithm problems might be guessing the category (tag) of the facing problems. Therefore, our study addresses the task of predicting the algorithm tag as a useful tool for engineers and developers. Moreover, we also consider predicting the difficulty levels of algorithm problems, which can be used as useful guidance to calculate the required time to solve that problem. In this paper, we present a real-world algorithm problem multi-task dataset, AMT, by mainly collecting problem samples from the most famous and large competitive programming website Codeforces. To the best of our knowledge, our proposed dataset is the most large-scale dataset for predicting algorithm tags compared to previous studies. Moreover, our work is the first to address predicting the difficulty levels of algorithm problems. We present a deep learning-based novel method for simultaneously predicting algorithm tags and the difficulty levels of an algorithm problem given. All datasets and source codes are available at https://github.com/sronger/PSG_Predicting_Algorithm_Tags_and_Difficulty.