The Efficiency of Fine-tuned LLMs on Patent Claim Generation

The article discusses a process for organizing data from JSON files into a CSV format, with claims in the input column and corresponding abstracts in the response column. It also includes fixed values for system and user prompts to be used during fine-tuning of language models (LLMs) on UbiAI’s platform. The tutorial involves fine-tuning both GPT4o-mini and the open-source Llama 3.1 models, comparing their performance to larger models like GPT-4o. UbiAI’s no-code platform facilitates the fine-tuning process, which typically takes 1 to 4 hours depending on dataset size. The platform supports both commercial and open-source LLM fine-tuning, allowing users to benchmark and select the best model for their tasks.

To test the fine-tuned models, a new unseen patent description from Google Patents is used, and the model is asked to generate claims based on it. The article emphasizes the ease of launching the training process on UbiAI’s platform and the ability to compare multiple fine-tuned models to determine the most suitable one for specific tasks.

Key takeaways

A script is used to extract content and claims from a JSON file and organize them into a CSV for fine-tuning LLMs.
UbiAI's platform is utilized for fine-tuning models like GPT4o-mini and Llama 3.1, comparing their performance to larger models.
The fine-tuning process on UbiAI's platform is no-code and can take 1 to 4 hours depending on dataset size.
Fine-tuned models are tested by generating claims from new patent descriptions, with UbiAI supporting both commercial and open-source LLMs.

The Efficiency of Fine-tuned LLMs on Patent Claim Generation

Key takeaways

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