Large language models (LLMs) basics for the Australian Tax Professional

Post Highlights

LLMs streamline tax tasks, transforming research, compliance, and communication.
Security and accuracy demand oversight to avoid errors and protect data.
Fine-tuning on Australian tax law ensures domain-relevant reliability.
AI solutions include automated drafting, summaries, and chatbots for efficiency.
Human expertise remains key to preserve quality and compliance.

Introduction

Large Language Models (LLMs) have quickly risen to prominence in the world of AI, offering powerful new ways to process, interpret, and generate human-like text. They underpin some of today’s most popular AI applications, including virtual assistants, automated writing tools, and advanced data-analysis platforms.

In the Australian tax industry, LLMs hold the promise of transforming core activities of the tax professional. Whether it is tax research, tax compliance or lodgement processes or client communication AI powered LLMs can make tasks faster and more efficient. However, adopting LLMs also comes with considerations around data security, model accuracy, and the complexities of training large-scale models on sensitive financial information.

This post briefly explores the basics of LLMs and how it impacts all Australian tax professionals from tax lawyers to mum and dad tax accountants.

Key Takeaways

LLMs in a Nutshell: Large Language Models, such as GPT-4, are advanced AI systems trained on massive text datasets. Their main goal is to predict the next word (or token) in a sequence, enabling them to write, summarise, or interpret text.
Parameters & Training: The “intelligence” of an LLM is largely determined by the number of parameters—think of these as the model’s internal “settings.” Training involves exposing these parameters to vast amounts of text so they can learn patterns and relationships within the data.
Tax Use Cases: LLMs can assist with tasks like drafting complex tax advice, summarising new legislation, or automating client communication.
Risks: Over-reliance on AI outputs can introduce errors if not verified, and data privacy remains a key concern. Proper oversight and quality assurance are essential.

Context & Relevance for Tax

Tax Research: The Australian tax environment is continuously evolving, with frequent updates to legislation, rulings, and compliance requirements. LLMs can help practitioners manage this complexity by quickly analysing large volumes of text—from ATO guidelines to legislative changes—and presenting distilled insights in plain English.
Workflow Efficiency: As the ATO itself becomes more technologically sophisticated, it’s critical for tax professionals to stay ahead of the curve. Adopting LLM-based tools can save time, reduce manual research effort, and improve overall accuracy in tax advice and filings.
Understand Risks: Tax professionals deal with highly sensitive financial data. Integrating LLMs into day-to-day operations raises questions about how data is fed into these models, the security protocols used to store or process that data, and the inherent risks of trusting an AI-generated output without human review.

Action Steps

Assess Your Tax Workflow Needs: Identify clear use cases—e.g., summarising tax rulings or automating client FAQs—before adopting an LLM tool.
Check Security: If using a cloud-based solution, verify how data is encrypted and stored. Consider working with vendors offering private or on-premise models.
Fine-Tune for Accuracy: Feed the AI relevant Australian tax legislation, rulings, and internal practice notes to improve its knowledge base.
Implement a Review Process: Always verify LLM-generated outputs. Establish a second layer of review for complex advisory work.
Stay Informed: Keep pace with emerging AI regulations and industry guidelines to ensure ongoing compliance.

Further Information

Demystifying LLM Parameters and Training

An LLM’s power is often correlated with its parameter count. These parameters are akin to dials that the model adjusts during training to recognise language patterns. Training involves exposing the model to vast datasets—including books, articles, and web content—so it learns how words and phrases connect. This allows LLMs to generate coherent, contextually relevant text in response to prompts.

For tax professionals, the training dimension matters. If an LLM hasn’t seen enough Australian tax legislation or relevant case law, it may generate incomplete or inaccurate advice. In response, some organisations explore fine-tuning—a secondary training process that customises the model with domain-specific texts. This can significantly improve accuracy but also demands rigorous data-security measures to protect sensitive or proprietary tax information used during the fine-tuning process.

Practical AI Solutions for Tax

Research and Summarisation: With frequent legislative updates, LLMs can sift through hundreds of pages of tax rulings or policy papers, summarising essential points within minutes. This allows practitioners to stay informed without manually reading every document.
Drafting and Reviewing Advice: LLMs can propose initial drafts of tax advice letters or compliance updates, speeding up mundane writing tasks. Practitioners can then refine these drafts, adding context and ensuring technical accuracy.
Client Communication: Tax agents or firms can deploy chatbots powered by LLMs to handle routine queries about deadlines, document requirements, or changes in tax rates. This frees up professionals to focus on more complex or high-value engagements.
Internal Knowledge Management: Large accounting or advisory firms often struggle with information silos. An LLM can serve as an internal “search engine,” pulling relevant precedents or compliance checklists from firm databases in response to natural language queries.

Potential Risks and Considerations

Data Privacy: Feeding client details or other sensitive data into a publicly hosted LLM could violate confidentiality obligations and data-protection laws. Consider using secure, enterprise-grade platforms that offer on-premise or encrypted solutions to protect information.
Accuracy and Liability: LLMs generate content based on patterns, not understanding. They may confidently produce incorrect or outdated advice if their training data is incomplete. Tax professionals must cross-check AI outputs to avoid liability for incorrect advice.
Model Bias: If an LLM’s training data skews toward a particular jurisdiction or was sourced from incomplete datasets, it might systematically overlook key Australian rules. Fine-tuning with local legislation and guidelines can help mitigate this risk.
Ethical and Professional Standards: Over-reliance on AI might erode professional judgment. Accountants and tax lawyers should treat LLM outputs as a starting point, applying their expertise to validate and tailor final recommendations.

Conclusion

Large Language Models offer a transformative opportunity for tax practitioners to streamline research, automate basic writing tasks, and enhance client interactions. Understanding how these models work—especially the role of parameters and the need for domain-specific training—can help maximise their usefulness and limit potential drawbacks. By coupling AI insights with professional expertise, tax professionals can gain a competitive edge in an increasingly complex legislative environment. Nonetheless, it’s crucial to maintain safeguards around data, enforce rigorous review processes, and uphold professional standards. Done right, LLMs can be a powerful ally, boosting both efficiency and client satisfaction.

Resource Links

By combining a foundational understanding of how LLMs function with sensible precautions around their use, tax practitioners can confidently integrate AI into their workflows and stay ahead in the evolving world of tax compliance and advisory.