{
  "annotation_id": "9012b9b7-395a-4305-b593-cf8dbdaff36d",
  "date_created": "2026-02-17T05:53:23.555000Z",
  "date_modified": "2026-02-17T05:53:23.555000Z",
  "file_hash": "0e3aee97acf3b5171be27828373babc96df714a6b3e93fdcb31f2ce9434231ed",
  "private": false,
  "record": {
    "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\u0027s 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.",
    "arxiv_id": "2601.10043",
    "authors": [
      "Zhiming Lian"
    ],
    "categories": [
      "q-fin.CP",
      "cs.LG"
    ],
    "license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
    "title": "Instruction Finetuning LLaMA-3-8B Model Using LoRA for Financial Named Entity Recognition",
    "url": "https://arxiv.org/abs/2601.10043",
    "version": "v1"
  },
  "schema_id": "dorsal/arxiv",
  "source": {
    "execution_id": "398ca5e4-7d90-4fbc-90a4-0983ea2aa14f",
    "id": "arXiv Dataset",
    "type": "Model",
    "variant": "snapshot-2026-01-17",
    "version": "0.1.0"
  },
  "user_id": 1000002
}