Introduction

This blog post is presented as an experiment to explore the capabilities of large language models (LLMs) in analyzing arbitration documents. It examines how AI can enhance understanding and provide actionable insights, particularly in identifying gaps in responses to the notice of arbitration. Artificial intelligence (AI) has revolutionized the legal field, offering powerful tools to assist lawyers in analyzing complex legal texts. While AI models like large language models (LLMs) are widely recognized for their ability to generate coherent and sophisticated text, their true strength lies in reading and understanding complex documents. By accurately interpreting dense legal texts, LLMs enable lawyers to identify critical insights with remarkable speed and precision. As legal cases become increasingly complex, AI has emerged as an invaluable asset, enabling lawyers to provide more accurate and timely legal services.

Comparing the Notice and the Response

The arbitration process demands a meticulous examination of both the notice of arbitration and the response to identify alignment or gaps. To explore this, we analyzed the Notice of Arbitration and the Response submitted in Sea Search-Armada, LLC v. Republic of Colombia, PCA Case No. 2023-37. By comparing the two documents, the GPT-4o model was used to pinpoint arguments that were addressed, partially addressed, or entirely omitted in the response. GPT-4o was selected for its ability to handle complex legal language and contextual nuances, making it particularly suitable for extracting insights and highlighting discrepancies. Our analysis focused on leveraging advanced prompting techniques to maximize the model’s accuracy and efficiency while acknowledging that the results required verification to ensure correctness.

AI Prompting Techniques

To ensure comprehensive and accurate analysis, we employed the following prompting strategies:

Providing Full Context: We attached the notice of arbitration and the response to ensure the model had all the necessary context for its analysis. This helped it identify unaddressed claims or arguments with precision.

• Example Prompt: "Compare the notice of arbitration with the response, identify gaps in addressing claims, and include page numbers for easy verification."

Refining the Analysis: We used follow-up prompts to ensure the model captured every argument. To enhance clarity, we requested the outputs in a table format, with exact page references for verification.

• Example Follow-Up: "Ensure all arguments are identified and organize them into a table, including page numbers."

Role-Based Guidance: The model was instructed to adopt the perspective of an arbitrator. This helped focus its analysis on procedural and substantive gaps, ensuring relevance to arbitration cases.

• Example Prompt: "As an arbitrator, highlight unresolved issues in the response, citing specific pages in the attached documents."

Accuracy and Efficiency: The GPT-4o model demonstrated high accuracy in identifying unaddressed arguments and procedural gaps. Key findings included:

• Precise identification of omitted treaty obligations such as FPS and NT/MFN.

• Accurate categorization of procedural deficiencies, including unaddressed proposals on arbitration seat and language.

In terms of efficiency, the model significantly reduced the time required for analysis:

• Manual review of similar cases typically spans several hours. With AI, this process was completed in under several minutes, though additional time was needed to double-check the AI-generated results for accuracy.

• Generated structured outputs, such as lists and summaries, that expedited the preparation of legal memoranda and case strategies.

Table of Addressed, Partially Addressed, and Unaddressed Arguments: Below is a comprehensive table that categorizes addressed and unaddressed arguments, along with page references from the arbitration documents: 

Time Savings and Practical Impact

The integration of AI drastically reduced the time required for comprehensive document analysis. While it took only five minutes to review the documents with the AI, manual analysis would typically take several hours, if not days, in traditional practice. This enables lawyers to:

• Focus on strategic decision-making rather than labor-intensive document review.

• Address unanticipated vulnerabilities in the arbitration response with agility.

Conclusion

The use of AI, as demonstrated in this analysis, showcases the transformative potential of advanced prompting techniques and LLM capabilities in arbitration. A key takeaway from this experiment is how AI highlighted unaddressed arguments with precision, offering insights that would have taken significantly longer to uncover manually. This underscores AI's potential to redefine efficiency in legal workflows. By streamlining document review and enhancing analytical precision, AI allows legal professionals to deliver more effective and timely strategies. However, the necessity to verify AI outputs highlights the importance of integrating tools with robust verification capabilities to reduce further the overall time spent. As AI tools continue to advance, their role in shaping the future of arbitration will only grow more significant.