# FineREX: Fine-Tuned NER-RE for Human Smuggling Knowledge Graphs

arXiv: https://arxiv.org/abs/2606.19710 · HTML: https://mlsanity.com/paper/2606.19710/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 26 references that are in the map.
- First posted on arXiv 18 Jun 2026.

## Builds on (most cited first, 14 of 14)

- [QLoRA: Efficient Finetuning of Quantized LLMs](https://mlsanity.com/paper/2305.14314/) (2023)
- [From Local to Global: A Graph RAG Approach to Query-Focused Summarization](https://mlsanity.com/paper/2404.16130/) (2024)
- [When Not to Trust Language Models: Investigating Effectiveness of Parametric and Non-Parametric Memories](https://mlsanity.com/paper/2212.10511/) (2022)
- [MindMap: Knowledge Graph Prompting Sparks Graph of Thoughts in Large Language Models](https://mlsanity.com/paper/2308.09729/) (2023)
- [Iterative Zero-Shot LLM Prompting for Knowledge Graph Construction](https://mlsanity.com/paper/2307.01128/) (2023)
- [From Single to Multi: How LLMs Hallucinate in Multi-Document Summarization](https://mlsanity.com/paper/2410.13961/) (2024)
- [Evaluating Position Bias in Large Language Model Recommendations](https://mlsanity.com/paper/2508.02020/) (2025)
- [Towards Practical GraphRAG: Efficient Knowledge Graph Construction and Hybrid Retrieval at Scale](https://mlsanity.com/paper/2507.03226/) (2025)
- [[Lions: 1] and [Tigers: 2] and [Bears: 3], Oh My! Literary Coreference Annotation with LLMs](https://mlsanity.com/paper/2401.17922/) (2024)
- [Relation Extraction with Fine-Tuned Large Language Models in Retrieval Augmented Generation Frameworks](https://mlsanity.com/paper/2406.14745/) (2024)
- [LINK-KG: LLM-Driven Coreference-Resolved Knowledge Graphs for Human Smuggling Networks](https://mlsanity.com/paper/2510.26486/) (2025)
- [Inside CORE-KG: Evaluating Structured Prompting and Coreference Resolution for Knowledge Graphs](https://mlsanity.com/paper/2510.26512/) (2025)
- [CORE-KG: An LLM-Driven Knowledge Graph Construction Framework for Human Smuggling Networks](https://mlsanity.com/paper/2506.21607/) (2025)
- [Combining Knowledge Graphs and NLP to Analyze Instant Messaging Data in Criminal Investigations](https://mlsanity.com/paper/2509.26487/) (2025)

## Abstract (arXiv)

Court proceedings contain valuable evidence about human smuggling networks, but this information is often buried within unstructured, jargon-heavy legal documents. While large language models (LLMs) can support knowledge graph construction through automated information extraction, existing approaches rely on general-purpose models that are not tailored to the entity and relationship definitions required in this domain. We introduce FineREX, a streamlined knowledge graph construction pipeline built around a fine-tuned LLM for named entity recognition and relationship extraction (NER-RE). Using a manually annotated dataset of 512 text chunks, FineREX achieves absolute improvements of 15.50% and 31.46% in entity and relationship F1-score, respectively, compared to a larger general-purpose baseline. These gains translate into higher-quality knowledge graphs, reducing legal noise by nearly half and lowering node duplication on long documents from 17.78% to 11.17%. By eliminating document rewriting and redundant extraction stages, FineREX also reduces end-to-end processing time by 50.0%. Our results demonstrate that domain-specific fine-tuning can substantially outperform larger general-purpose models while improving both the quality and efficiency of knowledge graph construction for illicit network analysis.