Fine-Tuned Language Models for Domain-Specific Summarization and Tagging

TL;DR

This paper introduces a pipeline combining fine-tuned large language models with named entity recognition to improve domain-specific text summarization and tagging, especially in evolving sub-cultural languages and slang.

Contribution

It demonstrates that instruction fine-tuning enhances summarization and tagging accuracy across general and domain-specific datasets, with transferability of reasoning capabilities across languages.

Findings

Instruction fine-tuning improves accuracy significantly.

Domain-specific fine-tuning outperforms general models.

Models effectively support real-time information management.

Abstract

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.