DiagHW: A Compact LLM for Hardware Failure Diagnosis via a Novel Knowledge Distillation Pipeline
- 30 January 2026
- book chapter
- Published by Springer Nature
- p. 393-407
Abstract
No abstract availableKeywords
This publication has 11 references indexed in Scilit:
- Evaluating LLMs and Prompting Strategies for Automated Hardware Diagnosis from Textual User-Reports2025
- An Ensemble of LLMs Finetuned with LoRA for NER in Portuguese Legal Documents2025
- Evaluation of Entry-Level Open-Source Large Language Models for Information Extraction from Digitized Documents2024
- FACTO Dataset: A Dataset of User Reports for Faulty Computer Components2024
- Challenges and barriers of using large language models (LLM) such as ChatGPT for diagnostic medicine with a focus on digital pathology – a recent scoping reviewDiagnostic Pathology, 2024
- Distilling Step-by-Step! Outperforming Larger Language Models with Less Training Data and Smaller Model Sizes2023
- Machine Learning-Based Approach for Hardware Faults PredictionIEEE Transactions on Circuits and Systems I: Regular Papers, 2020
- Evaluation of Recurrent Neural Networks for Hard Disk Drives Failure Prediction2018
- UiLog: Improving Log-Based Fault Diagnosis by Log AnalysisJournal of Computer Science and Technology, 2016
- Bootstrap Methods for Standard Errors, Confidence Intervals, and Other Measures of Statistical AccuracyStatistical Science, 1986