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Fine-tuning an LLM for patent drafting
The project seeks to optimize the performance of a Language Model (LLM) specifically tailored for patent drafting. Through meticulous fine-tuning, our objective is to elevate the LLM’s capabilities, thereby enhancing the efficiency and precision in the generation of patent documents.
Faculty Supervisor:
Reza Samavi;Arya Rahgozar
Student:
Partner:
XLSCOUT
Discipline:
Computer science
Sector:
Information and cultural industries
University:
Toronto Metropolitan University; University of Ottawa
Program:
Accelerate
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