On April 11, 2024, Bastian Best hosted a session of the Powerclaim Office Hours focused on the capabilities of large language models (LLMs) to draft patents in languages beyond English. This topic, pertinent to the global and multilingual nature of intellectual property (IP) law, reflects a growing interest in leveraging AI for more efficient patent drafting across different linguistic landscapes.
The Challenge of Multilingual Drafting
The discussion was sparked by a question from a previous seminar on whether LLMs can effectively draft patents in non-English languages. Given that most prominent LLMs are predominantly trained on English data, the ability to draft accurate and stylistically appropriate patents in other languages is a notable concern, especially for European practitioners who operate in multiple official languages.
Insights from Current LLMs
Bastian Best shared insights into the multilingual capabilities of GPT models from OpenAI and LLaMA 2 by Meta. Although GPT-4 demonstrates high performance across several languages, including German and French, the uneven distribution of training data—with a strong bias towards English—poses limitations for drafting patents in other languages with the same level of proficiency.
Practical Experimentation
A live experiment illustrated these points. Drafting a patent background section in English using Mistral 7B yielded polished and professional results. However, attempting the same task in German using the same model led to awkward phrasing, despite technical accuracy. The German output, while comprehensible, lacked the precision and style expected in patent documentation.
Specialized Models as a Solution
The session highlighted a more effective approach through the use of specialized models, specifically Em German Mistral, which is fine-tuned on German texts. This model produced significantly better results in German, both linguistically and stylistically, aligning closer with the expectations for patent documentation.
Broader Implications and Alternatives
Exploring specialized models for other languages, like French, remains a challenge due to the vast number of variants available. An alternative—drafting in English and then translating—was discussed, though it introduces additional complexity and potential for error.
Conclusion: Strategic Approaches to Multilingual Patent Drafting
The Powerclaim Office Hours underscored that while drafting patents in non-English languages with LLMs is possible, success depends heavily on choosing the right model, especially models fine-tuned for specific languages. This session provides valuable insights for IP practitioners and researchers looking to optimize their patent drafting processes with AI across different languages.
As the field of AI in IP law evolves, such discussions are crucial for developing effective, multilingual patent drafting strategies, marking a significant step forward in the integration of technology and intellectual property.
