SLMP_demo

This is the front-end code and back-end code of our paper《SLMP: A Scientific Literature Management Platform Based on Large Language Models》ICKG2024

Abstract:

This paper presents a Scientific Literature Management Platform (SLMP) based on large language models (LLMs). The platform consists of four modules: literature management, literature extraction, literature retrieval, and question answering. The core techniques used to support the four modules across the platform include a fine-tuned model PaperExtractGPT and a continual pre-training model ChatPaperGPT based on ChatGLM 2 using the data from scientific research literature, responsible for information extraction and communication, respectively. Due to their powerful capabilities in natural language understanding and generation, LLMs can understand complex scientific concepts based on the provided contexts, and thus generate high-quality texts and conduct in-depth information retrieval and question answering. Our platform can help researchers manage and utilize literature more effectively and efficiently for finding relevant literature, obtaining required information, and generating new knowledge.

front-end code

back-end code

Demo video

This is the demo video of paper.

Citation

Please cite the following paper if you use this code in your work.

@INPROCEEDINGS{10884340,
  author={Guo, Menghao and Jiang, Jinling and Wu, Fan and Sun, Shanxin and Zhang, Chen and Li, Wenhui and Sun, Zeyi and Chen, Guangyong and Wu, Xindong},
  booktitle={2024 IEEE International Conference on Knowledge Graph (ICKG)}, 
  title={SLMP: A Scientific Literature Management Platform Based on Large Language Models}, 
  year={2024},
  volume={},
  number={},
  pages={104-109},
  keywords={Training;Large language models;Knowledge graphs;Information retrieval;Data models;Question answering (information retrieval);Large Language Model;Literature Management and Retrieval;Question Answering},
  doi={10.1109/ICKG63256.2024.00021}}