How Large Language Models Revolutionize the Pre-review Process of Academic Papers: Intelligent Optimization from Submission to Peer Review

This article introduces an open-source project that uses Large Language Models (LLMs) to optimize the pre-review process of academic papers, aiming to solve the predicament of long review cycles and low efficiency of manual pre-review in the academic publishing industry. Based on the Transformer architecture, the project proposes three application directions, emphasizing AI assistance rather than replacing human editors, helping to improve the quality of manuscripts before peer review, and promoting a more efficient, transparent, and fair academic publishing ecosystem.

The academic publishing industry has long faced a core contradiction: high-quality peer review requires a lot of time and professional knowledge, but the growth in the number of submissions has overwhelmed editors and reviewers. The review cycle of top journals can last for months, delaying the dissemination of scientific research. Traditional pre-review relies on manual screening; repetitive work takes up editors' energy, making it difficult to ensure consistency and comprehensiveness.

The emergence of LLMs provides a possibility to solve this predicament. Models based on the Transformer architecture have strong text understanding and generation capabilities, and can be fine-tuned to adapt to specific domain needs. The core idea of the project is to embed LLMs into the pre-review process, undertake repetitive and rule-based checks, and free up human editors' energy to handle complex decisions. Technical implementation needs to consider the integration of domain knowledge (general LLMs require domain fine-tuning), interpretability requirements, the Transformer's self-attention mechanism to capture long-distance dependencies, multilingual support, etc.

The project proposes three application directions:

1. Format and Compliance Check: Quickly scan manuscripts to identify format inconsistencies, generate modification suggestions, and improve efficiency and standard uniformity;
2. Initial Content Quality Screening: Analyze the novelty of research questions, rationality of methods, etc., provide preliminary quality assessment, and help identify obviously unqualified manuscripts;
3. Domain Matching Analysis: Judge the matching degree with the journal's domain through topics, keywords, and cited literature, assist authors in journal selection and editors in manuscript distribution.

If the technology is widely applied, the academic publishing ecosystem will undergo fundamental changes: authors can receive automated pre-review feedback to improve submission success rates; journals will have shorter review cycles and lower operating costs; small journals and open-access journals can use automated tools to reduce costs, gain fair opportunities in competition, and promote the democratization of academic publishing.

Current technology has limitations: LLMs may generate hallucinations (incorrect suggestions) and inherit biases from training data. Future development directions include more refined domain customization, multimodal content processing (charts and formulas), deep integration with citation management software/plagiarism checking systems, promoting the redesign of academic publishing processes, and forming a more efficient, transparent, and fair ecosystem.