FinEmbedDiff: A Cost-Effective Approach of Classifying Financial Documents with Vector Sampling using Multi-modal Embedding Models
<|reference_start|>FinEmbedDiff: A Cost-Effective Approach of Classifying Financial Documents with Vector Sampling using Multi-modal Embedding Models: Accurate classification of multi-modal financial documents, containing text, tables, charts, and images, is crucial but challenging. Traditional text-based approaches often fail to capture the complex multi-modal nature of these documents. We propose FinEmbedDiff, a cost-effective vector sampling method that leverages pre-trained multi-modal embedding models to classify financial documents. Our approach generates multi-modal embedding vectors for documents, and compares new documents with pre-computed class embeddings using vector similarity measures. Evaluated on a large dataset, FinEmbedDiff achieves competitive classification accuracy compared to state-of-the-art baselines while significantly reducing computational costs. The method exhibits strong generalization capabilities, making it a practical and scalable solution for real-world financial applications.<|reference_end|>
@article{biswas2024finembeddiff:,
    title={FinEmbedDiff: A Cost-Effective Approach of Classifying Financial
  Documents with Vector Sampling using Multi-modal Embedding Models},
    author={Anjanava Biswas, Wrick Talukdar},
    journal={International Research Journal of Modernization in Engineering
  Technology and Science: Vol. 06 (2024): No. 5, 6142-6152},
    year={2024},
    doi={10.56726/IRJMETS57269},
    archivePrefix={arXiv},
    eprint={2406.01618},
    primaryClass={cs.IR cs.AI}
}