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The Surprising Efficiency of Fine-Tuned LLMs on Patent Claim Generation
How fine-tuned LLMs can outperform larger models in patent claim generation
As LLM models grow larger and more complex, aligning them with specific tasks presents notable challenges. A pertinent example is the drafting of patent claims, which must succinctly and precisely outline the scope of an invention while adhering to legal and technical standards.
Patent claims play a crucial role in the patent filing process by defining the boundaries of intellectual property protection. They enable examiners, attorneys, and potential competitors to understand the scope of an innovation and determine its uniqueness. Seasoned patent attorneys develop this skill over years, balancing intricate details with concise language.
Attempts to automate this process using standard GPT models have encountered difficulties. For instance, research indicates that while GPT-4 can generate patent claims, their effectiveness significantly diminishes with subsequent dependent claims.
These findings underscore the limitations of relying solely on prompt engineering with vanilla GPT models for such nuanced tasks. The generated claims frequently omit critical details, include ambiguities, or fail to meet the stringent requirements…
