Comparative evaluation of large language models for biotechnology review writing
Year: 2026
Type: article
Abstract: Large language models (LLMs) are transforming how biotechnology review articles are conceived and written. This study evaluates LLMs in generating scientific review articles across three case studies with the focus on biomanufacturing and microbiology. Structured prompts were applied across models under “Deep Research” modes, and both statistical and manual analysis (e.g., writing structure, citation metrics, critical depth, citation validity, and hallucination rate) were conducted. Results revealed that while LLMs can summarize large volumes of literature and generate structured outputs with coherent flow and illustrative tables, they fell short in critical analysis, quantitative reasoning, and citation reliability. “Pro” versions produced more accurate and extensive citations than base versions, though issues of redundancy, bias toward specific publishers (e.g., MDPI, Frontiers), and occasional fabrication persisted. Among models, GPT-5 Pro was deeper in its analysis but had fewer citations. Gemini 2.5 Pro and the Perplexity Pro search engine retrieved broader literature with limited critique. Qwen 3 Max and DeepSeek R1 offered moderate balance, with the latter having higher hallucination rates in the application programming interface (API) version. Overall, “pro” versions of LLMs generate decent summaries of reviewing topics but lack scholarly standards of novelty, rigor, and citation accuracy. Strategic integration and cross-validation of LLMs improve the quality of review papers. The findings underscore the need for new opinions, quantitative analysis or simulation, and interdisciplinary knowledge synthesis to publish valuable biotechnology reviews. • GPT, Gemini, DeepSeek, Qwen, and Perplexity are compared for biotech review writing. • “Pro” versions of LLMs are good at summarizing information but lack critical analysis. • LLMs tend to favor citing open-access journals. • Cross-validation and prompt selection among different LLMs improve review quality. • Quality review publications should focus on novel perspectives and quantitative analysis.
Source: Biotechnology Advances
Institution: Washington University in St. Louis
Language: English
Subfield: History and Philosophy of Science
Field: Arts and Humanities
Domain: Social Sciences
Open Access status: hybrid
Estimated APC paid: $5,460
Funder: National Science Foundation
OpenAlex ID: W7127400844