Large Language Models Empower Academic Publishing: Research on Automated Pre-Review Systems for Scientific Manuscripts

This project focuses on optimizing academic publishing processes, exploring the application of Transformer-based large language models to the pre-review stage of academic manuscripts. It constructs an automated system covering three dimensions—format review, language polishing, and content compliance check—aiming to improve the efficiency and quality control level of academic publishing.

Over 3 million academic papers are published globally each year, with a 5% annual growth rate, but the efficiency of the publishing process lags behind—the average cycle from submission to publication ranges from several months to a year. The pre-review stage requires editors to spend a lot of time on repetitive tasks such as format, language, and references, which has become a bottleneck. In addition, the contradiction between the surge in manuscripts and limited editorial resources is prominent; the rejection rate of high-impact journals exceeds 90%, and balancing quality and efficiency is a major industry challenge.

Transformer-based large language models represented by GPT, BERT, and T5 have strong natural language understanding and generation capabilities. Compared to traditional rule-based systems, their advantages include: understanding complex semantics in context, generalizing to new fields without a large amount of labeled data, multi-language support, and improving professionalism through fine-tuning—providing possibilities for automated processing of academic texts.

The system covers three core dimensions:

1. Format and structure compliance check: Automatically parse documents, verify chapter structure, chart specifications, citation formats, and metadata integrity, and generate revision suggestions;
2. Language quality assessment and polishing: Grammar correction, academic style optimization, clarity improvement, and terminology consistency check, using a suggestion mode to retain authors' autonomy;
3. Content compliance and academic norm screening: Plagiarism detection, conflict of interest statement check, ethical review certificate verification, data availability statement check, and author contribution statement verification.

A modular architecture is adopted, with core components including:

• Document parsing engine: Supports formats such as PDF/Word, extracting structure and content in layers;
• LLM inference layer: Can access open-source models (LLaMA, Falcon, etc.), commercial APIs (GPT-4, Claude, etc.), and domain-specific models;
• Hybrid architecture: Rule engines handle tasks with clear formats (e.g., citation formats), while LLMs handle semantic understanding tasks (e.g., language polishing), balancing efficiency and intelligence.

The test set consists of real manuscripts from different disciplines, with evaluation metrics including accuracy, recall, false positive rate, and processing efficiency. The results show: format check accuracy exceeds 95%; 80% of language polishing suggestions are recognized by professional editors; manual pre-review takes 30-60 minutes per article, while the system only takes 5-10 minutes, significantly shortening the cycle.

Technical limitations: Domain specificity (large differences in norms across disciplines), limited complex reasoning ability (e.g., judging the rationality of research design), and weak multi-modal content understanding; Ethical considerations: Responsibility attribution, fairness (whether there is bias), and human-machine boundaries. The system is positioned as an 'editorial assistant', with final decision-making power in humans, and suggestions are traceable.

Industry impact of the system: Publishers reduce workload and shorten cycles; authors receive instant feedback to improve submission success rates; the academic community promotes normative consistency; open science enhances the discoverability of results. Future vision: Human-machine collaboration, where AI undertakes repetitive work and humans focus on professional judgment, promoting more efficient and fair academic publishing.