arXiv:2606.10392v1 Announce Type: new Abstract: Financial named-entity recognition (NER) is essential for translating unstructured financial reports and news into structured knowledge graphs. However, general-purpose large language models (LLMs) often misclassify financial entities or ignore domain-specific patterns. This paper investigates the use of DeepSeek-R1-8B, a recent open-source large language model, combined with Low-Rank Adaptation (LoRA) and Noisy Embedding Fine-Tuning (NEFTune) for financial NER. Each annotated sentence in our corpus of 1693 samples is converted into an instructio
The proliferation of open-source large language models and advanced fine-tuning techniques is enabling more specialized AI applications, making domain-specific improvements crucial for their real-world utility.
This development indicates progress in making LLMs more precise for critical tasks like financial named-entity recognition, directly impacting the automation and accuracy of financial data analysis.
General-purpose LLMs are becoming more adaptable and accurate for niche, high-value applications through targeted fine-tuning, reducing their prior tendency to misclassify domain-specific patterns.
- · Financial data analytics firms
- · Open-source AI community
- · NLP researchers
- · DeepSeek-R1-8B users
- · Providers of less customizable or specialized NER solutions
- · Companies relying on manual financial data structuring
Improved accuracy in financial named-entity recognition using fine-tuned open-source LLMs.
Faster and more reliable conversion of unstructured financial information into structured knowledge graphs, enhancing algorithmic trading and risk assessment.
Increased demand for specialized AI training data and expertise in fine-tuning open-source models for various industry-specific applications, potentially leading to new service markets.
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Read at arXiv cs.AI