Fine-Tuned Thoughts: Leveraging Chain-of-Thought Reasoning for Industrial Asset Health Monitoring (2025)

Abstract: Small Language Models (SLMs) are becoming increasingly popular in specialized fields, such as industrial applications, due to their efficiency, lower computational requirements, and ability to be fine-tuned for domain-specific tasks, enabling accurate and cost-effective solutions. However, performing complex reasoning using SLMs in specialized fields such as Industry 4.0 remains challenging. In this paper, we propose a knowledge distillation framework for industrial asset health, which transfers reasoning capabilities via Chain-of-Thought (CoT) distillation from Large Language Models (LLMs) to smaller, more efficient models (SLMs). We discuss the advantages and the process of distilling LLMs using multi-choice question answering (MCQA) prompts to enhance reasoning and refine decision-making. We also perform in-context learning to verify the quality of the generated knowledge and benchmark the performance of fine-tuned SLMs with generated knowledge against widely used LLMs. The results show that the fine-tuned SLMs with CoT reasoning outperform the base models by a significant margin, narrowing the gap to their LLM counterparts. Our code is open-sourced at: https://github.com/IBM/FailureSensorIQ.

## introduction large language models (llms) have demonstrated exceptional proficiency in both generic and specialized domains due to their extensive pretraining on vast amounts of text data from diverse sources, that enables strong contextual understanding and reasoning. small language models (slms), on the other hand, while perform well in common nlp tasks @xcite such as text classification, sentiment analysis, their limited parameter capacity (≤8b) constrains their ability to store extensive knowledge and perform complex reasoning, making them less effective for specialized domains without substantial modifications. recent studies on knowledge distillation from industries and academics have shown that small language models (slms) hold great potential in specialized domains, such as math reasoning @xcite . the reduced computational requirements of slms allow for faster inference and deployment on resource-constrained devices. also, slms can be fine-tuned, hosted and operated locally on computing machines, minimizing the need for sensitive user data and domain information to be exposed or leaked. the benefits of slms bring opportunities to the manufacturing industries, including maintenance and monitoring, process optimization, and quality control. ## related work chain-of-thought (cot) prompting has significantly improved interactions with large language models (llms), leading to better results across various datasets, such as mathqa @xcite . this success has inspired research focused on utilizing cot traces from larger models to distill information, knowledge, and reasoning @xcite ) @xcite . the core premise of this research is that information for a target domain can be either pre-existing in the form of questionanswer pairs or generated using an additional llm. aligning an llm to a specific skill has recently emerged as another area of focus. knowledge generation from a teacher llm typically begins by leveraging documents or seed instructions @xcite , @xcite . more recently, the magpie-based approach has gained interest, allowing llms to generate alignment data in an instruction-free manner @xcite . however, extracting domain-specific knowledge from llms remains a challenge. various attempts have been made to generate domain-specific aligned models, such as meditron-70b for the medical domain @xcite and entigraph cpt for long-passage question answering on articles @xcite , among others. these approaches typically rely on large-scale corpora with billions of tokens or begin with a few million tokens to generate additional synthetic data using a teacher model. however, they largely overlook the industry 4.0 domain. one primary reason for this may be the lack of a qualitatively constructed validation dataset such as other domains pubmedqa @xcite ) and chemical safety (e.g., chemsafety @xcite ) as examples. ## methodology we present our proposed knowledge distillation framework in figure 1 , which transfers fmea knowledge from a teacher model to a student model. the process for generating synthetic multiplechoice question answering (mcqa) data using chain of thought (cot) prompting is described step-by-step in the following sections. notably, the generation process is seed-free, meaning it does not rely on an initial dataset. ## experiments in this section, we conduct extensive experiments to evaluate the effectiveness of the fmea-related qa data generated by failuresensoriq (appendix: ## conclusion we present an innovative knowledge distillation framework designed for asset health monitoring tasks. our framework generates high-quality synthetic data using llms without relying on initial knowledge documents for assets. by fine-tuning small language models (slms) with this domainspecific data, the models exhibit reduced hallucination, improved reasoning accuracy, and enhanced consistency in responses. cot distillation on multichoice qa tasks further strengthens the slm's contextual understanding of industrial entities. the low cost of slm qlora fine-tuning (less than 1 hour per experiment, under 4gb adapter for 8b models) makes it a practical solution for adapting models to industry tasks while maintaining scalability and efficiency. despite these advantages, challenges remain, particularly in handling perturbations, suggesting that future work could focus on perturbation-aware training or incorporating more diverse perturbation scenarios into the synthetic data generation process.