# Understanding the Role of Temperature in Diverse Question Generation by GPT-4

arXiv: https://arxiv.org/abs/2404.09366 · HTML: https://mlsanity.com/paper/2404.09366/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 6 references that are in the map.
- First posted on arXiv 14 Apr 2024.

## Builds on (most cited first, 3 of 3)

- [Experiences from Using Code Explanations Generated by Large Language Models in a Web Software Development E-Book](https://mlsanity.com/paper/2211.02265/) (2022)
- [A Comparative Study of AI-Generated (GPT-4) and Human-crafted MCQs in Programming Education](https://mlsanity.com/paper/2312.03173/) (2023)
- [Can We Trust AI-Generated Educational Content? Comparative Analysis of Human and AI-Generated Learning Resources](https://mlsanity.com/paper/2306.10509/) (2023)

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

- [Can Large Language Models Match Tutoring System Adaptivity? A Benchmarking Study](https://mlsanity.com/paper/2504.05570/) (2025)
- [Ontologies in Design: How Imagining a Tree Reveals Possibilites and Assumptions in Large Language Models](https://mlsanity.com/paper/2504.03029/) (2025)
- [EvoGPT: Leveraging LLM-Driven Seed Diversity to Improve Search-Based Test Suite Generation](https://mlsanity.com/paper/2505.12424/) (2025)
- [Grade Guard: A Smart System for Short Answer Automated Grading](https://mlsanity.com/paper/2504.01253/) (2025)
- [Language-Aware Token Boosting: LLM Language Confusion Reduction Without Tuning](https://mlsanity.com/paper/2606.08994/) (2026)

## Abstract (arXiv)

We conduct a preliminary study of the effect of GPT's temperature parameter on the diversity of GPT4-generated questions. We find that using higher temperature values leads to significantly higher diversity, with different temperatures exposing different types of similarity between generated sets of questions. We also demonstrate that diverse question generation is especially difficult for questions targeting lower levels of Bloom's Taxonomy.