mlsanityData to 7 Oct 2026

SafeLLM: Domain-Specific Safety Monitoring for Large Language Models: A Case Study of Offshore Wind Maintenance

arXiv 2410.10852 ↗First posted 6 Oct 2024cs.CL cs.AIcited by 4

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Abstract (arXiv)

The Offshore Wind (OSW) industry is experiencing significant expansion, resulting in increased Operations & Maintenance (O&M) costs. Intelligent alarm systems offer the prospect of swift detection of component failures and process anomalies, enabling timely and precise interventions that could yield reductions in resource expenditure, as well as scheduled and unscheduled downtime. This paper introduces an innovative approach to tackle this challenge by capitalising on Large Language Models (LLMs). We present a specialised conversational agent that incorporates statistical techniques to calculate distances between sentences for the detection and filtering of hallucinations and unsafe output. This potentially enables improved interpretation of alarm sequences and the generation of safer repair action recommendations by the agent. Preliminary findings are presented with the approach applied to ChatGPT-4 generated test sentences. The limitation of using ChatGPT-4 and the potential for enhancement of this agent through re-training with specialised OSW datasets are discussed.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 14 of its 30 references that are in the map. First posted on arXiv 6 Oct 2024.

Builds on

14 of 30 references.

References · 14

  1. 2013Efficient Estimation of Word Representations in Vector Spacecited by 4,346
  2. 2023G-Eval: NLG Evaluation using GPT-4 with Better Human Alignmentcited by 2,205
  3. 2020On Faithfulness and Factuality in Abstractive Summarizationcited by 895
  4. 2023Safety-Tuned LLaMAs: Lessons From Improving the Safety of Large Language Models that Follow Instructionscited by 377
  5. 2022Red-Teaming the Stable Diffusion Safety Filtercited by 257
  6. 2018Statistical Aspects of Wasserstein Distancescited by 202
  7. 2021Understanding the Capabilities, Limitations, and Societal Impact of Large Language Modelscited by 159
  8. 2023Self-contradictory Hallucinations of Large Language Models: Evaluation, Detection and Mitigationcited by 123
  9. 2022SafeText: A Benchmark for Exploring Physical Safety in Language Modelscited by 41
  10. 2023Chainpoll: A high efficacy method for LLM hallucination detectioncited by 39
  11. 2023Weakly Supervised Detection of Hallucinations in LLM Activationscited by 20
  12. 2022Guidance on the Safety Assurance of Autonomous Systems in Complex Environments (SACE)cited by 7
2 more
  1. 2022Keep your Distance: Determining Sampling and Distance Thresholds in Machine Learning Monitoringcited by 4
  2. 2022A Deep Learning Framework for Wind Turbine Repair Action Prediction Using Alarm Sequences and Long Short Term Memory Algorithmscited by 1

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 3; 2026: 1.

4 papers.

  1. 2025A Comparative Benchmark of Large Language Models for Labelling Wind Turbine Maintenance Logscited by 3
  2. 2026Bayesian Uncertainty Propagation for Agentic RAG Pipelines: A Proof-of-Concept Study on Multi-Hop Question Answeringcited by 2
  3. 2025Exploratory Semantic Reliability Analysis of Wind Turbine Maintenance Logs using Large Language Modelscited by 1
  4. 2025Seeing the Unseen: Towards Training-Free Inspection for Wind Turbine Blades Using Knowledge-Augmented Vision Language Modelscited by 0