Instruction Finetuning LLaMA-3-8B Model Using LoRA for Financial Named Entity Recognition

ArXiv ID: 2601.10043 “View on arXiv”

Authors: Zhiming Lian

Abstract

Particularly, financial named-entity recognition (NER) is one of the many important approaches to translate unformatted reports and news into structured knowledge graphs. However, free, easy-to-use large language models (LLMs) often fail to differentiate organisations as people, or disregard an actual monetary amount entirely. This paper takes Meta’s Llama 3 8B and applies it to financial NER by combining instruction fine-tuning and Low-Rank Adaptation (LoRA). Each annotated sentence is converted into an instruction-input-output triple, enabling the model to learn task descriptions while fine-tuning with small low-rank matrices instead of updating all weights. Using a corpus of 1,693 sentences, our method obtains a micro-F1 score of 0.894 compared with Qwen3-8B, Baichuan2-7B, T5, and BERT-Base. We present dataset statistics, describe training hyperparameters, and perform visualizations of entity density, learning curves, and evaluation metrics. Our results show that instruction tuning combined with parameter-efficient fine-tuning enables state-of-the-art performance on domain-sensitive NER.

Keywords: Financial Named-Entity Recognition (NER), Low-Rank Adaptation (LoRA), Instruction Fine-tuning, Large Language Models, Knowledge Graphs, N/A (Natural Language Processing)

Complexity vs Empirical Score

• Math Complexity: 3.0/10
• Empirical Rigor: 7.5/10
• Quadrant: Street Traders
• Why: The paper’s mathematical content is limited to describing standard neural architectures and LoRA decomposition, lacking advanced derivations or novel theoretical frameworks. Empirical rigor is high, featuring a concrete dataset with 1,693 annotated sentences, comparative benchmarks against multiple models, hyperparameter reporting, and visualizations of learning curves and evaluation metrics.

  flowchart TD
    A["Research Goal: Financial Named-Entity Recognition<br/>using LLaMA-3-8B & LoRA"] --> B["Data Preparation<br/>1,693 annotated sentences<br/>Instruction-Input-Output format"]
    B --> C["Methodology<br/>Instruction Fine-tuning + LoRA"]
    C --> D["Training Process<br/>Parameter-efficient updates<br/>Low-rank matrices"]
    D --> E["Computational Evaluation<br/>Micro-F1 Score = 0.894<br/>vs. Qwen3-8B, Baichuan2-7B, BERT"]
    E --> F["Key Outcomes<br/>State-of-the-art Financial NER<br/>Efficient domain adaptation"]