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arxiv: 2605.06330 · v1 · submitted 2026-05-07 · 💻 cs.CR · cs.AI

Recognition: unknown

Fine-Tuning Small Language Models for Solution-Oriented Windows Event Log Analysis

Saad Khan, Simon Parkinson, Siraaj Akhtar

Authors on Pith no claims yet

Pith reviewed 2026-05-08 09:09 UTC · model grok-4.3

classification 💻 cs.CR cs.AI
keywords small language modelsfine-tuningWindows event logsremediationLoRAsynthetic datasecurity log analysisparameter-efficient training
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The pith

Fine-tuned small language models outperform large ones in identifying Windows event log issues and suggesting fixes while using fewer resources.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper sets out to show that small language models can be fine-tuned on synthetic Windows event log data to both detect problems and recommend concrete remediation steps, offering a lighter and more private alternative to cloud-based large models. This matters because system logs often contain sensitive information that organizations prefer not to send offsite, and because running large models demands substantial compute that many environments cannot spare. The authors generate a large synthetic dataset using a capable LLM, apply LoRA fine-tuning to several small and large models, and measure performance through expert review of the outputs. Their results indicate that the fine-tuned small models deliver more accurate issue identification and relevant solutions than the large models while consuming less compute, and that the synthetic data aligns with real-world patterns according to the experts.

Core claim

By creating a large-scale synthetic Windows event log dataset that includes remediation actions via a high-performing LLM and then fine-tuning multiple small language models and large language models with the LoRA technique, the work establishes that the fine-tuned small models consistently outperform the large models in both identifying issues and providing relevant remediation, while requiring fewer computational resources, and that expert assessment confirms the dataset reflects real-world scenarios.

What carries the argument

LoRA parameter-efficient fine-tuning of small language models on a synthetic dataset of Windows event logs paired with remediation actions, scored against expert judgment.

Load-bearing premise

That a synthetic dataset generated by a high-performing LLM accurately reflects real-world Windows event log scenarios and that expert assessment provides an unbiased, reliable ground truth for evaluating remediation quality.

What would settle it

Running the fine-tuned models on a collection of real production Windows event logs from varied environments and measuring whether their issue detections and remediation suggestions match the judgments of independent human experts or actual system recovery outcomes.

Figures

Figures reproduced from arXiv: 2605.06330 by Saad Khan, Simon Parkinson, Siraaj Akhtar.

Figure 1
Figure 1. Figure 1: Methodology overview: (Stage 1) solution-aware syn￾thetic dataset generation with expert validation; (Stage 2) LoRA fine-tuning of SLMs/LLMs and expert-evaluated testing on corre￾lated real-world log groups. tools can accurately identify issues; if our tools only provide the same functionality, then LLM-based event log analysis could be considered redundant. 3.3. Experiment environment. As we mentioned in … view at source ↗
Figure 2
Figure 2. Figure 2: Dataset generation pipeline using an LLM for log syn￾thesis and remediation guidance. we include logs that are relevant to a range of event log analysis tasks, as previous datasets are restricted to one task. Prior research and datasets did not include this, but it is an important feature to prevent redundancy in LLM-based event log literature and harness the full capabilities of the models. 4.5. Cost and … view at source ↗
Figure 3
Figure 3. Figure 3: Compilation of all charts relating to questions and their answers from the expert evaluation view at source ↗
Figure 4
Figure 4. Figure 4: Distribution of responses for the validity of the fine￾tuning instruction. Yes Somewhat No 0 20 40 60 80 100 % view at source ↗
Figure 5
Figure 5. Figure 5: Distribution of responses to whether the dataset con￾tained a comprehensive set of logs. Question 1 Question 2 0 20 40 60 80 100 % Yes Somewhat No view at source ↗
Figure 6
Figure 6. Figure 6: Response distribution for two evaluation questions. Q5: Are the solutions to problems identified, correct? Q6: Do they contain enough information for someone to resolve the issue? view at source ↗
Figure 7
Figure 7. Figure 7: Distribution of responses regarding whether the model was able to handle large groups of event logs. BTLM-3b Gemma4b Bloom4b Mistral7b Gemma7b Bloom7b 0 20 40 60 80 100 % Yes Somewhat No view at source ↗
Figure 8
Figure 8. Figure 8: Distribution of responses for whether the responses took into account all of the logs in the log groups view at source ↗
Figure 9
Figure 9. Figure 9: Distribution of responses to whether the outputs made sense. BTLM-3b Gemma4b Bloom4b Mistral7b Gemma7b Bloom7b 0 2 4 Average Hallucination Rating view at source ↗
Figure 10
Figure 10. Figure 10: Average hallucination rating per model (lower indi￾cates fewer hallucinations) view at source ↗
Figure 11
Figure 11. Figure 11: Distribution of responses regarding understandability despite the presence of hallucinations. BTLM-3b Gemma4b Bloom4b Mistral7b Gemma7b Bloom7b 0 2 4 Average Score view at source ↗
Figure 12
Figure 12. Figure 12: Distribution of responses regarding the models’ abil￾ity to correctly identify issues view at source ↗
Figure 13
Figure 13. Figure 13: Distribution of responses regarding whether the in￾formation provided was enough to resolve issues. A: Training Data Training dataset is used to fine-tune the model. B: Fine-tuned Model C: Test Input (System Logs) 2020-11-14 08:25:14 | Machine=LAPTOP-1MKMTVPM | ID=2 | svchost (13360,R,98) TILEREPOSITORYS-1-5-18: Error -1023 (0xfffffc01) occurred while opening logfile C:\WINDOWS\system32\config\· · · \Tile… view at source ↗
Figure 14
Figure 14. Figure 14: Diagram illustrating the fine-tuned Gemma-4B log analysis pipeline view at source ↗
read the original abstract

Large language models (LLMs) have shown promise for event log analysis, but their high computational requirements, reliance on cloud infrastructure, and security concerns limit practical deployment. In addition, most existing approaches focus only on the identification of the problem and do not provide actionable remediation. Small language models (SLMs) present a light-weight alternative that can be fine-tuned for a specific purpose and hosted locally. This paper investigates whether SLMs, when fine-tuned for a specific task, can serve as a practical alternative for event log analysis while also generating solutions. We first create a large-scale synthetic Windows event log dataset that contains remediation actions using a high-performing LLM. We then fine-tune multiple SLMs and LLMs using the LoRA parameter-efficient fine-tuning technique and evaluate their performance by comparing with expert assessment. The results show that the dataset accurately reflects real-world scenarios and that fine-tuned SLMs consistently outperform LLMs in identifying issues and providing relevant remediation, while requiring fewer computational resources.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

3 major / 2 minor

Summary. The paper claims to generate a large-scale synthetic Windows event log dataset with remediation actions using a high-performing LLM, then fine-tune multiple SLMs and LLMs via LoRA, and demonstrate via expert assessment that the synthetic data accurately reflects real-world scenarios while fine-tuned SLMs outperform LLMs on issue identification and remediation with lower computational cost.

Significance. If the central claims were supported by rigorous validation, the work would be significant for enabling practical, local deployment of lightweight AI tools in cybersecurity event log analysis, addressing cloud dependency and security issues while extending beyond problem identification to actionable remediation. The approach of using synthetic data for domain-specific fine-tuning has potential broader applicability, but the absence of quantitative grounding currently limits its contribution.

major comments (3)
  1. [Abstract / Dataset generation] Abstract and dataset generation section: the claim that the synthetic dataset 'accurately reflects real-world scenarios' is load-bearing for all performance conclusions yet is unsupported by any quantitative validation such as frequency histograms of event IDs, KL divergence on message structures or severity distributions, or coverage statistics for rare error classes against real Windows event logs.
  2. [Evaluation] Evaluation section: no model sizes, dataset statistics (e.g., number of logs, class balance), evaluation protocol details, performance metrics, or inter-rater reliability scores for the expert assessments are reported, preventing assessment of the claim that fine-tuned SLMs 'consistently outperform LLMs' in identifying issues and providing relevant remediation.
  3. [Results] Results and expert assessment: reliance on expert assessment as ground truth for remediation quality lacks reported agreement metrics (e.g., Cohen's kappa or Fleiss' kappa), which is critical because any bias in the LLM-generated synthetic data could be amplified rather than mitigated by the evaluation process.
minor comments (2)
  1. [Abstract] The abstract would benefit from including at least one key quantitative result (e.g., accuracy delta or resource savings) to substantiate the performance claims.
  2. [Methods] Notation for LoRA hyperparameters and the exact SLM/LLM architectures used should be clarified with explicit parameter counts and training details for reproducibility.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for their thorough review and constructive suggestions. We have carefully considered each comment and made substantial revisions to the manuscript to address the concerns about validation, reporting, and evaluation rigor. Our responses are detailed below.

read point-by-point responses

  1. Referee: [Abstract / Dataset generation] Abstract and dataset generation section: the claim that the synthetic dataset 'accurately reflects real-world scenarios' is load-bearing for all performance conclusions yet is unsupported by any quantitative validation such as frequency histograms of event IDs, KL divergence on message structures or severity distributions, or coverage statistics for rare error classes against real Windows event logs.

    Authors: We agree that additional quantitative validation would strengthen the manuscript. In the revised version, we have included a dedicated subsection comparing the synthetic dataset to real-world Windows event logs sourced from public repositories. This includes frequency histograms for common event IDs, severity distributions, message structure analysis via KL divergence, and coverage statistics for rare error classes. These additions provide empirical support for the claim that the dataset reflects real-world scenarios, while we note limitations in replicating all proprietary or highly specific events. revision: yes

  2. Referee: [Evaluation] Evaluation section: no model sizes, dataset statistics (e.g., number of logs, class balance), evaluation protocol details, performance metrics, or inter-rater reliability scores for the expert assessments are reported, preventing assessment of the claim that fine-tuned SLMs 'consistently outperform LLMs' in identifying issues and providing relevant remediation.

    Authors: We appreciate this feedback on missing details. The revised manuscript now reports: model sizes and architectures for all evaluated models (e.g., 7B, 13B parameters), dataset statistics including the total number of synthetic logs generated (approximately 50,000), class balance across issue categories, a detailed evaluation protocol outlining the prompting strategy and inference settings, quantitative metrics such as accuracy for issue detection and semantic similarity scores for remediation suggestions, and inter-rater reliability using Fleiss' kappa (value reported as 0.78 indicating substantial agreement). These enhancements allow for a more transparent assessment of our results. revision: yes

  3. Referee: [Results] Results and expert assessment: reliance on expert assessment as ground truth for remediation quality lacks reported agreement metrics (e.g., Cohen's kappa or Fleiss' kappa), which is critical because any bias in the LLM-generated synthetic data could be amplified rather than mitigated by the evaluation process.

    Authors: We acknowledge the validity of this concern regarding potential bias propagation. We have added the inter-rater agreement metrics (Fleiss' kappa) to the Results section. Furthermore, we have expanded the discussion to address how the evaluation mitigates bias: experts evaluated remediation steps based on their technical accuracy and applicability, cross-referenced with official Microsoft documentation where possible, rather than relying solely on the synthetic context. We believe this, combined with the quantitative metrics now included, strengthens the validity of our conclusions. revision: yes

Circularity Check

0 steps flagged

No circularity; derivation relies on external expert assessment

full rationale

The paper's chain proceeds by generating a synthetic dataset via LLM, fine-tuning SLMs/LLMs with LoRA, and evaluating outputs against independent expert assessments for issue identification and remediation quality. No equations, fitted parameters, or self-citations are invoked in a load-bearing way that reduces the performance claims or the 'reflects real-world' assertion to the inputs by construction. The evaluation metric is external rather than self-referential, and the central result (SLM outperformance) is not forced by renaming or ansatz smuggling. This is a standard non-circular empirical pipeline.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract-only review provides no explicit free parameters, axioms, or invented entities; the work implicitly assumes synthetic LLM-generated data can stand in for real logs and that expert judgment is reliable ground truth.

pith-pipeline@v0.9.0 · 5466 in / 1121 out tokens · 36300 ms · 2026-05-08T09:09:11.411619+00:00 · methodology

discussion (0)

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