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Large Language Model Fine-Tuning for Domain-Specific Knowledge Extraction in Renewable Energy, Medical, and Financial Research Literature

Nader Behdad
Volume 5

Abstract

Domain-specific research literature in renewable energy, medical sciences, and finance contains extensive specialized knowledge, including technical terminology, methodological descriptions, experimental findings, clinical indicators, financial risk factors, performance metrics, and evidence-based conclusions. However, extracting accurate and context-aware information from such literature remains challenging due to domain-specific vocabulary, semantic ambiguity, long document structures, rapidly expanding publication volumes, and the limitations of general-purpose natural language processing models in understanding specialized scientific content. This paper proposes a large language model fine-tuning framework for domain-specific knowledge extraction across renewable energy, medical, and financial research literature. The proposed framework adapts pre-trained transformer-based language models to specialized corpora using supervised fine-tuning, instruction tuning, and parameter-efficient learning strategies such as Low-Rank Adaptation and adapter-based fine-tuning. The system supports multiple knowledge extraction tasks, including named entity recognition, relation extraction, keyphrase extraction, evidence summarization, methodology classification, citation-aware retrieval, and domain-specific question answering. Large-scale corpora were constructed from peer-reviewed journal articles, conference papers, clinical studies, renewable energy forecasting reports, financial risk assessment literature, and benchmark scientific datasets. A comprehensive preprocessing pipeline was applied, including document parsing, abstract and section segmentation, metadata extraction, duplicate removal, terminology normalization, annotation refinement, and train-validation-test splitting to ensure robust evaluation. Experimental results demonstrate that the proposed fine-tuned language model achieves superior performance compared with general-purpose language models, traditional NLP pipelines, BiLSTM-CRF models, BERT-based architectures, SciBERT, BioBERT, and finance-oriented transformer baselines across multiple extraction tasks. The proposed framework records higher precision, recall, F1-score, exact-match accuracy, semantic similarity, and factual consistency, while reducing hallucinated outputs and improving the reliability of extracted information in technical contexts. Furthermore, explainability and traceability mechanisms were incorporated through attention analysis, confidence scoring, evidence-span highlighting, and citation-grounded response generation, allowing users to verify extracted knowledge against the original literature. The findings indicate that fine-tuned large language models provide a scalable, accurate, and adaptable solution for domain-specific knowledge extraction, offering practical value for researchers, clinicians, renewable energy analysts, financial experts, systematic reviewers, and intelligent literature-mining platforms operating across complex scientific domains.

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References

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