AI-Powered Taxation Assistant

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📖 References ​

This page lists all academic and technical works cited throughout the AI-Powered Taxation Assistant documentation.
It provides a formal bibliography (APA-style) for research reproducibility and academic evaluation.

🧠 Retrieval-Augmented Generation (RAG) & LLMs ​

  1. Lewis, P., Perez, E., Piktus, A., Petroni, F., Karpukhin, V., et al. (2020).
    Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks.
    Advances in Neural Information Processing Systems (NeurIPS 2020).
    🔗 NeurIPS Paper

  2. Gao, T., Yao, Y., & Chen, D. (2023).
    Retrieval-Augmented Generation for Large Language Models: A Survey.
    arXiv preprint arXiv:2312.10997.
    🔗 arXiv

  3. Wang, H., Zhang, R., & Li, X. (2024).
    CBR-RAG: Case-Based Reasoning for Retrieval-Augmented Generation.
    arXiv preprint arXiv:2402.06196.
    🔗 arXiv

📊 Financial Document & Table Extraction ​

  1. Paliwal, S., Vishwanathan, S., & Anand, R. (2020).
    TableNet: Deep Learning Model for End-to-End Table Detection and Structure Recognition from Images.
    arXiv preprint arXiv:2005.06457.
    🔗 arXiv

  2. Riba, P., Dutta, A., & Lladós, J. (2019).
    Graph Neural Networks for Table Detection in Invoices.
    Proceedings of ICDAR Workshops 2019.
    🔗 arXiv

  3. Liu, C., Huang, T., & Wang, L. (2024).
    PdfTable: A Unified Toolkit for Table Extraction from Financial Documents.
    arXiv preprint arXiv:2403.10254.
    🔗 arXiv

💼 Financial NLP & Domain Models ​

  1. Araci, D. (2020).
    FinBERT: Financial Sentiment Analysis with Pre-Trained Language Models.
    arXiv preprint arXiv:2006.08097.
    🔗 arXiv

  2. Shah, P., & Aggarwal, R. (2024).
    Survey of Deep Learning Methods for Document and Table Detection.
    ACM Transactions on Intelligent Systems and Technology.
    🔗 ACM Digital Library

⚖️ Explainability & Ethical AI ​

  1. Parker, C., Cho, S., & Vasarhelyi, M. (2022).
    Explainable Artificial Intelligence (XAI) in Auditing.
    International Journal of Accounting Information Systems, 44, 100572.
    🔗 DOI: 10.1016/j.accinf.2022.100572

  2. Górski, M. (2024).
    Exploring Explainable AI in the Tax Domain.
    SSRN Electronic Journal.
    🔗 SSRN Preprint

  1. Zhou, L., Patel, S., & Lin, H. (2024).
    Interpretable Long-Form Legal QA with Retrieval-Augmented Generation.
    arXiv preprint arXiv:2404.01208.
    🔗 arXiv

  2. Li, Q., Zhang, T., & Kim, D. (2025).
    RAG-Enhanced Evidence Recommendation in Financial Contexts.
    ACM Conference on Financial Computing 2025.
    🔗 ACM Digital Library