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DOI: 10.4230/OASIcs.DX.2026.14

Using SLMs and LLMs for Diagnosis

Authors: Sophia Plank and Franz Wotawa

Published in: OASIcs, Volume 148, 37th International Conference on Principles of Diagnosis and Resilient Systems (DX 2026)

Abstract

Large Language Models (LLMs) have rapidly transformed how organizations approach complex problem solving, raising the question of whether they can also be applied to technical reasoning tasks. In this paper, we investigate whether LLMs can be used for system diagnosis, i.e., the localization of faults in systems comprising interacting components. In particular, we conducted an initial study using examples ranging from electric circuits to heating systems, evaluating 13 different language models of varying sizes with respect to the correctness and completeness of their diagnosis. The experimental evaluation revealed that language models with more parameters achieve superior performance.

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Sophia Plank and Franz Wotawa. Using SLMs and LLMs for Diagnosis. In 37th International Conference on Principles of Diagnosis and Resilient Systems (DX 2026). Open Access Series in Informatics (OASIcs), Volume 148, pp. 14:1-14:17, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2026)

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@InProceedings{plank_et_al:OASIcs.DX.2026.14,
  author =	{Plank, Sophia and Wotawa, Franz},
  title =	{{Using SLMs and LLMs for Diagnosis}},
  booktitle =	{37th International Conference on Principles of Diagnosis and Resilient Systems (DX 2026)},
  pages =	{14:1--14:17},
  series =	{Open Access Series in Informatics (OASIcs)},
  ISBN =	{978-3-95977-455-0},
  ISSN =	{2190-6807},
  year =	{2026},
  volume =	{148},
  editor =	{Pill, Ingo and Zanella, Marina and Provan, Gregory},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.DX.2026.14},
  URN =		{urn:nbn:de:0030-drops-278284},
  doi =		{10.4230/OASIcs.DX.2026.14},
  annote =	{Keywords: LLM diagnosis performance, SLM diagnosis performance, comparative study}
}

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Using SLMs and LLMs for Diagnosis

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