The Large Language Model for Scientific Literature:Method,Framework,and Application
The emergence of the Large Language Model (LLM) has profoundly transformed knowledge production,the methods by which users acquire knowledge and intelligence,and the analysis and service of scientific literature.This paper systematically reviews the progress of LLM based in domain-specific applications,summarizes their technical approaches and application scenarios,and analyzes the practical needs and value of LLM for scientific literature.The paper proposes and designs a technological framework for constructing LLM for scientific literature,addressing two key issues:the standardized construction of a scientific literature corpus and multi-round incremental fine-tuning training.Furthermore,it pre-trains a LLM for scientific literature and develops an intelligent knowledge service platform called Spark Science Research Assistant based on this LLM.The paper has the following innovative achievements:first,it explored the construction method of scientific literature corpus by utilizing the raw data resources of large-scale scientific literature.This involves building a pre-training corpus and fine-tuning instruction sets from the levels of full-text paragraph text,language steps,and reading comprehension Q&A,thereby achieving pre-training and fine-tuning of the LLM for scientific literature;second,based on the pre-trained LIM,the Spark Science Research Assistant platform was developed.This platform demonstrates effectiveness in multiple typical research scenarios,including literature review,literature knowledge extraction,literature comparative analysis,academic writing and polishing,multilingual translation,proofreading of papers,intelligent pre-review of the full paper.Moreover,the model showcases strong interdisciplinary capabilities,facilitating cross-domain knowledge transfer and integration.The paper provides technical and scenario-based references for building a smart scientific research environment system.Future directions include improving the model's performance,expanding its knowledge base,and integrating it with other AI technologies,paving the way for more advanced and comprehensive AI-assisted research tools.6 figs.1 tab.33 refs.
Large Language Model for scientific literatureScientific literature corpusAIKnowledge serviceSpark Science Research Assistant
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