arXiv:2606.19710v1 Announce Type: new Abstract: 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 recognit
The increasing sophistication of LLMs allows for their fine-tuning on highly specific and sensitive domains, such as legal documents related to human smuggling, enabling more targeted information extraction.
This development demonstrates how advanced AI can be tailored to extract actionable intelligence from previously intractable unstructured data, enhancing the effectiveness of law enforcement and intelligence operations.
The ability to automatically construct knowledge graphs from complex legal proceedings will significantly accelerate the identification and analysis of human smuggling networks, shifting from manual review to automated insight generation.
- · Law Enforcement Agencies
- · Intelligence Analysts
- · AI/ML Developers specializing in NLP
- · Organizations combating human trafficking
- · Human Smuggling Networks
Law enforcement gains a powerful new tool for intelligence gathering and operational planning against organized crime.
Increased efficiency in prosecuting human trafficking cases due to clearer evidence and network mapping.
Potential for similar specialized LLM applications to be developed for other complex legal or investigative domains, driving demand for fine-tuned AI solutions in public security.
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Read at arXiv cs.CL