# Temperature in SLMs: Impact on Incident Categorization in On-Premises Environments

arXiv: https://arxiv.org/abs/2511.19464 · HTML: https://mlsanity.com/paper/2511.19464/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 8 references that are in the map.
- First posted on arXiv 21 Nov 2025.

## Builds on (most cited first, 4 of 4)

- [A Survey of Large Language Models](https://mlsanity.com/paper/2303.18223/) (2023)
- [The Effect of Sampling Temperature on Problem Solving in Large Language Models](https://mlsanity.com/paper/2402.05201/) (2024)
- [Exploring the Impact of Temperature on Large Language Models:Hot or Cold?](https://mlsanity.com/paper/2506.07295/) (2025)
- [Contextual Temperature for Language Modeling](https://mlsanity.com/paper/2012.13575/) (2020)

## Abstract (arXiv)

SOCs and CSIRTs face increasing pressure to automate incident categorization, yet the use of cloud-based LLMs introduces costs, latency, and confidentiality risks. We investigate whether locally executed SLMs can meet this challenge. We evaluated 21 models ranging from 1B to 20B parameters, varying the temperature hyperparameter and measuring execution time and precision across two distinct architectures. The results indicate that temperature has little influence on performance, whereas the number of parameters and GPU capacity are decisive factors.