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

• URL: http://arxiv.org/abs/2511.19464v1
• Date: Fri, 21 Nov 2025 19:37:09 GMT
• Title: Temperature in SLMs: Impact on Incident Categorization in On-Premises Environments
• Abstract summary: We investigate whether locally executed SLMs can meet this challenge.<n>Results indicate that temperature has little influence on performance, whereas the number of parameters and GPU capacity are decisive factors.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

Related papers

• Adaptive Rigor in AI System Evaluation using Temperature-Controlled Verdict Aggregation via Generalized Power Mean [51.56484100374058]
  This paper presents a method that combines a five-level verdict-scoring system with generalized power-mean aggregation and an intuitive temperature parameter T [0.1, 1.0] to control evaluation rigor.<n>Low temperatures yield pessimistic scores suited for safety-critical domains; high temperatures produce lenient scores appropriate for conversational AI.
  arXiv  Detail & Related papers  (2026-04-04T09:04:38Z)
• Temperature as a Meta-Policy: Adaptive Temperature in LLM Reinforcement Learning [47.83947232413507]
  Temperature controls the trade-off between exploration and exploitation in large language models (LLMs)<n>High temperatures encourage diverse but noisy outputs, while low temperatures produce focused outputs but may cause premature convergence.<n>We propose Temperature Adaptive Meta Policy Optimization (TAMPO), a new framework that recasts temperature control as a learnable meta-policy.
  arXiv  Detail & Related papers  (2026-02-12T09:59:58Z)
• TempPerturb-Eval: On the Joint Effects of Internal Temperature and External Perturbations in RAG Robustness [3.796841688047966]
  This work presents a systematic investigation of how text perturbations interact with temperature settings across multiple LLM runs.<n>We propose a comprehensive RAG Perturbation-Temperature Analysis Framework that subjects retrieved documents to three distinct perturbation types across varying temperature settings.<n>Our work yields three key contributions: (1) a diagnostic benchmark for assessing RAG robustness, (2) an analytical framework for quantifying perturbation-temperature interactions, and (3) practical guidelines for model selection and parameter tuning under noisy retrieval conditions.
  arXiv  Detail & Related papers  (2025-12-01T01:46:36Z)
• Grounded Test-Time Adaptation for LLM Agents [75.62784644919803]
  Large language model (LLM)-based agents struggle to generalize to novel and complex environments.<n>We propose two strategies for adapting LLM agents by leveraging environment-specific information available during deployment.
  arXiv  Detail & Related papers  (2025-11-06T22:24:35Z)
• Analyzing the Effects of Supervised Fine-Tuning on Model Knowledge from Token and Parameter Levels [72.3670919950349]
  Large language models (LLMs) acquire substantial world knowledge during pre-training.<n>Post-training techniques such as supervised fine-tuning (SFT) shape this knowledge change behavior.<n>We evaluate closed-book question answering (CBQA) performance across five LLMs from the LLaMA-2 and LLaMA-3 families.
  arXiv  Detail & Related papers  (2025-09-20T09:40:32Z)
• Deep Learning-Enhanced for Amine Emission Monitoring and Performance Analysis in Industrial Carbon Capture Plants [0.6533091401094101]
  We present data driven deep learning models for forecasting and monitoring amine emissions and key performance parameters in amine-based post-combustion carbon capture systems.<n>For emission prediction, models were designed for 2-amino-2-methyl-1-propanol (AMP) and Piperazine emissions measured via FTIR and IMR-MS methods.<n>These models achieved high predictive accuracy exceeding 99% and effectively tracked both steady trends and abrupt fluctuations.
  arXiv  Detail & Related papers  (2025-09-05T16:57:54Z)
• Sensitivity Meets Sparsity: The Impact of Extremely Sparse Parameter Patterns on Theory-of-Mind of Large Language Models [55.46269953415811]
  We identify ToM-sensitive parameters and show that perturbing as little as 0.001% of these parameters significantly degrades ToM performance.<n>Our results have implications for enhancing model alignment, mitigating biases, and improving AI systems designed for human interaction.
  arXiv  Detail & Related papers  (2025-04-05T17:45:42Z)
• Monte Carlo Temperature: a robust sampling strategy for LLM's uncertainty quantification methods [1.3892342684177872]
  We propose a robust sampling strategy that eliminates the need for temperature calibration.<n>MCT provides more robust uncertainty estimates across a wide range of temperatures.<n>MCT achieves statistical parity with oracle temperatures, which represent the ideal outcome of a well-tuned but computationally expensive HPO process.
  arXiv  Detail & Related papers  (2025-02-25T17:33:20Z)
• Optimizing Temperature for Language Models with Multi-Sample Inference [47.14991144052361]
  This paper addresses the challenge of automatically identifying the (near)-optimal temperature for different large language models.<n>We provide a comprehensive analysis of temperature's role in performance optimization, considering variations in model architectures, datasets, task types, model sizes, and predictive accuracy.<n>We propose a novel entropy-based metric for automated temperature optimization, which consistently outperforms fixed-temperature baselines.
  arXiv  Detail & Related papers  (2025-02-07T19:35:25Z)
• Enhancing Large Language Model Performance with Gradient-Based Parameter Selection [32.88329156118533]
  Gradient-Mask Tuning (GMT) is a method that selectively updates parameters during training based on their gradient information.<n>Our empirical results across various tasks demonstrate that GMT not only outperforms traditional fine-tuning methods but also elevates the upper limits of LLM performance.
  arXiv  Detail & Related papers  (2024-06-21T17:42:52Z)
• The Effect of Sampling Temperature on Problem Solving in Large Language Models [0.0]
  We investigate the effect of sampling temperature on the performance of Large Language Models (LLMs) on various problem-solving tasks. Despite anecdotal reports to the contrary, our empirical results indicate that changes in temperature from 0.0 to 1.0 do not have a statistically significant impact on LLM performance.
  arXiv  Detail & Related papers  (2024-02-07T19:11:23Z)
• Dynamically Scaled Temperature in Self-Supervised Contrastive Learning [11.133502139934437]
  We focus on improving the performance of InfoNCE loss in self-supervised learning by proposing a novel cosine similarity dependent temperature scaling function. Experimental evidence shows that the proposed framework outperforms the contrastive loss-based SSL algorithms.
  arXiv  Detail & Related papers  (2023-08-02T13:31:41Z)
• Learning IMM Filter Parameters from Measurements using Gradient Descent [45.335821132209766]
  In intrinsic parameters of targets under track can be completely unobservable until the system is deployed. With state-of-the-art sensor systems growing more and more complex, the number of parameters naturally increases. In this paper, the parameters of an interacting multiple model (IMM) filter are optimized solely using measurements.
  arXiv  Detail & Related papers  (2023-07-13T08:35:40Z)
• A Three-regime Model of Network Pruning [47.92525418773768]
  We use temperature-like and load-like parameters to model the impact of neural network (NN) training hyper parameters on pruning performance. A key empirical result we identify is a sharp transition phenomenon: depending on the value of a load-like parameter in the pruned model, increasing the value of a temperature-like parameter in the pre-pruned model may either enhance or impair subsequent pruning performance. Our model reveals that the dichotomous effect of high temperature is associated with transitions between distinct types of global structures in the post-pruned model.
  arXiv  Detail & Related papers  (2023-05-28T08:09:25Z)
• Lidar Light Scattering Augmentation (LISA): Physics-based Simulation of Adverse Weather Conditions for 3D Object Detection [60.89616629421904]
  Lidar-based object detectors are critical parts of the 3D perception pipeline in autonomous navigation systems such as self-driving cars. They are sensitive to adverse weather conditions such as rain, snow and fog due to reduced signal-to-noise ratio (SNR) and signal-to-background ratio (SBR)
  arXiv  Detail & Related papers  (2021-07-14T21:10:47Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.