Editorial
SEP-LLM: Professional QA in the SEP Domain Using Retrieval-Augmented LLMs
@ARTICLE{10.4108/eetsis.10653, author={Chenchen Guo and Kehao Wang and Dianhui Mao and Yunlong Xiong and Yiwen Lyu and Junhua Chen}, title={SEP-LLM: Professional QA in the SEP Domain Using Retrieval-Augmented LLMs}, journal={EAI Endorsed Transactions on Scalable Information Systems}, volume={12}, number={10}, publisher={EAI}, journal_a={SIS}, year={2026}, month={5}, keywords={Standard Essential Patent(SEP), Retrieval-Augmented Generation (RAG), Knowledge Graph, Low-Rank Adaptation (LoRA), Instruction Fine-Tuning}, doi={10.4108/eetsis.10653} }- Chenchen Guo
Kehao Wang
Dianhui Mao
Yunlong Xiong
Yiwen Lyu
Junhua Chen
Year: 2026
SEP-LLM: Professional QA in the SEP Domain Using Retrieval-Augmented LLMs
SIS
EAI
DOI: 10.4108/eetsis.10653
Abstract
INTRODUCTION: Question answering tasks in the Standard Essential Patent (SEP) domain impose high demands on models for professional terminology comprehension, regulatory interpretation, and factual accuracy. Existing general-purpose large language models show limitations in this field, mainly in knowledge retrieval accuracy, semantic matching, and legal compliance of generated content. Therefore, there is an urgent need to develop a specialized intelligent QA system tailored for the SEP domain. OBJECTIVES: This paper aims to develop an intelligent QA system for the SEP domain, SEP-LLM, to improve knowledge retrieval, semantic matching, and content compliance, providing high-quality automated answers to SEP-related questions. METHODS: We collected and curated a large set of SEP-related regulations, technical standards, and judicial cases to build a high-quality QA dataset. Leveraging the LightRAG framework, a large language model was used to extract entities and relationships from documents, constructing a structured SEP knowledge graph with incremental updates to ensure dynamic completeness. In retrieval, a two-layer strategy addresses both fine-grained entity queries and broader thematic searches, improving accuracy and coverage. In generation, DeepSeek-LLM-7B was fine-tuned with LoRA on SEP-specific instructions and terminology, enhancing the model’s understanding and generation capabilities while significantly reducing training and inference resource requirements. RESULTS: Experimental results demonstrate that SEP-LLM significantly outperforms leading general-purpose models, including GPT-4o and Qwen3-235B, across three key metrics: BLEU-4, ROUGE-L, and Accuracy. These findings underscore its superior performance and promising potential for professional Quality Assurance within SEP domain. CONCLUSION: The LightRAG-based SEP-LLM system effectively enhances knowledge retrieval, semantic understanding, and compliance in SEP QA tasks, demonstrating the potential of retrieval-augmented generation techniques in specialized domains and providing a practical solution for intelligent information services in the SEP field.
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