Shangsheng Gao
ABSTRACT
LLM (Large Language Model) is developing rapidly today, and it is very important to use LLM to help existing question answering systems improve. The government question answering system is of great value in improving government administrative efficiency. Existing intelligent questions and answers mostly use the combination of keyword matching and machine learning. But keyword matching is difficult to understand complex and multi-round dialogue questions, resulting in limited quality of reply content. Based on machine learning method, it has further improved the understanding of semantics, but because the understanding of words in the field of government affairs is too difficult and professional; and the semantic recognition of colloquial questions is difficult, the performance is not even as good as the question answering system based on keyword matching. This paper uses the large language model as a tool to help understand user questions, and integrates it into the existing government question answering system. On the premise of effectively utilizing the advantages of the large language model, it avoids its defects. And it is demonstrated by experiments that the above system has a great improvement compared with the previous method.
- Antoine Bordes, Nicolas Usunier, Alberto Garcia-Duran, Jason Weston, and Oksana Yakhnenko. 2013. Translating embeddings for modeling multi-relational data. Advances in neural information processing systems 26 (2013).Google Scholar
- André Guskow Cardoso. 2023. Do we need a Chat-GPT-Gov? The importance of technology for effective access to public information.The importance of technology for effective access to public information.(January 7, 2023) (2023).Google Scholar
- Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2018. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018).Google Scholar
- Sepp Hochreiter and Jürgen Schmidhuber. 1997. Long short-term memory. Neural computation 9, 8 (1997), 1735–1780.Google ScholarDigital Library
- Po-Sen Huang, Xiaodong He, Jianfeng Gao, Li Deng, Alex Acero, and Larry Heck. 2013. Learning deep structured semantic models for web search using clickthrough data. In Proceedings of the 22nd ACM international conference on Information & Knowledge Management. 2333–2338.Google ScholarDigital Library
- Kishore Papineni, Salim Roukos, Todd Ward, and Wei-Jing Zhu. 2002. Bleu: a method for automatic evaluation of machine translation. In Proceedings of the 40th annual meeting of the Association for Computational Linguistics. 311–318.Google Scholar
- Georgios Patsoulis, Rafail Promikyridis, and Efthimios Tambouris. 2021. Integration of chatbots with Knowledge Graphs in eGovernment: The case of Getting a Passport. In 25th Pan-Hellenic Conference on Informatics. 425–429.Google ScholarDigital Library
- Anestis Stamatis, Alexandros Gerontas, Anastasios Dasyras, and Efthimios Tambouris. 2020. Using chatbots and life events to provide public service information. In Proceedings of the 13th International Conference on Theory and Practice of Electronic Governance. 54–61.Google ScholarDigital Library
- Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 (2017).Google Scholar
- Mo Yu, Wenpeng Yin, Kazi Saidul Hasan, Cicero dos Santos, Bing Xiang, and Bowen Zhou. 2017. Improved neural relation detection for knowledge base question answering. arXiv preprint arXiv:1704.06194 (2017).Google Scholar
Index Terms
- Application of large language model in intelligent Q&A of digital government
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