Computer Science

Computer Science ›› 2026, Vol. 53 ›› Issue (9): 92-100.doi: 10.11896/jsjkx.250600103

• Research and Application of Large Language Model Technology • Previous Articles     Next Articles

Domain-adapted and Dynamically Retrieval-augmented Approach for Large-scale History Discipline Model Construction

GUO Yuyang1, SHI Lei1, LIU Huan1, DONG Yixiang1, LI Rui2   

  1. 1 School of Computer Science and Technology,Xi'an Jiaotong University,Xi'an 710049,China
    2 School of Continuing Education,Xi'an Jiaotong University,Xi'an 710049,China
  • Received:2025-06-16 Revised:2025-11-02 Online:2026-09-15 Published:2026-09-10
  • About author:GUO Yuyang,born in 1998,postgraduate.His main research interests include natural language processing and the application of LLM in smart education.
    LIU Huan,born in 1990,associate professor,Ph.D supervisor, is a member of CCF(No.E9713M).His main research interests include LLM and smart education.
  • Supported by:
    National Natural Science Foundation of China(62137002,62192781).

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Abstract: Currently,the informatization of educatio continues to develop in depth,artificial intelligence technologies are providing new methodologies for subject-specific education.Among them,large language models(LLMs) demonstrate great potential in constructing knowledge systems and supporting discipline-specific tasks.However,general-purpose LLMs face core challenges in educational settings,including blurred knowledge boundaries,significant factual deviations,and inadequate task adaptation.To address these issues,this paper focuses on the history discipline and proposes a domain-specific LLM construction and dynamic knowledge-enhanced generation approach.The proposed method consists of two core modules:subject-specific model construction and generation optimization.Firstly,an incremental pre-training dataset is built through semantic deduplication and in-context learning.A domain adaptation framework based on instruction tuning is introduced,utilizing a cross-paragraph causal coherence modeling strategy to integrate global document semantics with local features for model training.A multi-dimensional evaluation system for subject-specific models is also developed.Secondly,a dynamic decision-based retrieval-enhanced generation mechanism is proposed.This includes an instruction reconstruction module for better user intent parsing,and a multi-scenario dynamic retrieval decision module for intelligent,on-demand routing.Finally,a chain-of-thought-guided two-stage filtering mechanism is employed to extract high-value knowledge.Experiments on history-related tasks demonstrate that,compared with general LLMs,the proposed method improves multiple-choice question accuracy by 1.43% to 10.19% and boosts BLEU scores in generative tasks by 1.02% to 43.35%.Compared with traditional retrieval-augmented approaches,it achieves gains of 3.54% to 20.47% in selection tasks and 0.18% to 6.33% in generation tasks.These results effectively mitigate knowledge bias and the challenge of cove-ring low-frequency knowledge,offering a new paradigm of dynamic knowledge enhancement for building domain-specific educational LLMs.

Key words: Large language models, Disciplinary large models, Instruction tuning, Large model evaluation, Retrieval-augmented generation

CLC Number: 

  • TP391
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