""Large Language Models can be used to analyze the vast amount of data on glycoengineering strategies and identify the most effective approaches for specific applications. This can be achieved through dimension reduction and combinatorial optimization techniques.", "Large Language Models can be used to optimize protein production in glycoengineered plants by identifying the most effective combinations of genetic elements, such as promoters and untranslated regions (UTRs)."" L3.1-405B . The objective of Part B was to generate useful tables that were based on 5 different authors' actual numerical results compared to LLM forecasts of monoclonal antibody improvement using monte carlo simulations and markov chain models, as shown in Prompts B1 and B2. Scoring was primarily based on table completion, detail, and how convincing comparisons were. Figure displays the cumulative judging scores of the top three models, while Table and Table are ChatGPT-4o and 3.5 Sonnet generated tables formatted to close approximations using LaTeX, followed by critiques from the manuscript author.