面向领域特定摘要与标注的微调语言模型
Fine-Tuned Language Models for Domain-Specific Summarization and Tagging
AI总结：
针对亚文化语言与俚语干扰领域信息提取的问题，提出融合微调LLM与NER的领域文本摘要标注流水线，经多数据集微调与指标验证，该方法可提升处理准确率，具备跨语言迁移性与实时适配性，适用于知识管理与安全运营场景。
摘要：
本文提出了一种将微调大语言模型（LLM）与命名实体识别（NER）相结合的流水线，用于高效的领域特定文本摘要与标注。作者旨在应对亚文化语言与俚语快速演变带来的挑战——这类语言会提升自动化信息提取与执法监测的难度。研究借助LLaMA Factory框架，在通用数据集与定制的领域特定数据集（尤其是政治与安全领域）上对LLM进行微调。研究采用BLEU和ROUGE指标评估模型，结果表明指令微调显著提升了摘要与标注的准确率，在专用语料上效果尤为突出。值得注意的是，LLaMA3-8B-Instruct模型尽管最初在中文理解上存在局限，但经过领域特定微调后表现优于经过中文训练的同类模型，说明底层推理能力可跨语言迁移。该流水线可生成简洁摘要与结构化实体标注，助力文档快速分类与分发。该方法具备可扩展性与适配性，适用于实时应用场景，可支撑高效信息管理，满足捕捉新兴语言趋势的持续需求。LLM与NER的融合为将非结构化文本转化为可执行洞察提供了可靠方案，这对现代知识管理与安全运营至关重要。
英文摘要：
This paper presents a pipeline integrating fine-tuned large language models (LLMs) with named entity recognition (NER) for efficient domain-specific text summarization and tagging. The authors address the challenge posed by rapidly evolving sub-cultural languages and slang, which complicate automated information extraction and law enforcement monitoring. By leveraging the LLaMA Factory framework, the study fine-tunes LLMs on both generalpurpose and custom domain-specific datasets, particularly in the political and security domains. The models are evaluated using BLEU and ROUGE metrics, demonstrating that instruction fine-tuning significantly enhances summarization and tagging accuracy, especially for specialized corpora. Notably, the LLaMA3-8B-Instruct model, despite its initial limitations in Chinese comprehension, outperforms its Chinese-trained counterpart after domainspecific fine-tuning, suggesting that underlying reasoning capabilities can transfer across languages. The pipeline enables concise summaries and structured entity tagging, facilitating rapid document categorization and distribution. This approach proves scalable and adaptable for real-time applications, supporting efficient information management and the ongoing need to capture emerging language trends. The integration of LLMs and NER offers a robust solution for transforming unstructured text into actionable insights, crucial for modern knowledge management and security operations.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
