Artificial Intelligence Master of History

Yuming Zou

doi:10.62051/ijcsit.v4n3.03

Authors

Yuming Zou

DOI:

https://doi.org/10.62051/ijcsit.v4n3.03

Keywords:

Artificial Intelligence, Large language models (LLMs), Historical education

Abstract

In this EPQ, I built a system that successively enables the large language model to answer questions on the past with evidence from primary sources. This project was motivated by the realization that despite the complexity, the performance of the large language model (LLMs) literally means understanding but simply does pattern recognition, coupled with probabilistic text prediction. Against this backdrop, a specialized AI was developed to help in error correction, given that AI-produced historical data are generally full of errors, by the use of the open-source Langchain-Chatchat that allows integration of LLMs with a structured knowledge database. These contain factual and historical information needed for the AI to compute the accurate answer. The system uses the GLM-4 model by Tsinghua University for the level of GPT-4 and above. This web application is built on Streamlit, an AI-powered system that interacts with the user. The steps through which input queries and historical text were processed by the system include an Unstructured Loader, a Text Splitter, an embedding model, and a Vector Store. Successful implementation of this system not only improved the reliability in AI for historical education but also laid the foundation for further improvement in educational technology and AI interaction models. Future plans will involve further historical database expansion to even more diverse periods and geographies, as well as enhancing the user interface to become more interactive and accessible.

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References

[1] Oguzhan Topsakal and T. Akinci (2023). Creating Large Language Model Applications Utilizing LangChain: A Primer on Developing LLM Apps Fast. Journal of Information and Computational Science, DOI:10.59287/icaens.1127.

[2] Yuanhao Gong (2023). Dynamic Large Language Models on Blockchains. arXiv preprint arXiv:2307.10549, DOI:10.48550/arXiv.2307.10549.

[3] Bo Xu and M. Poo (2023). Large language models and brain-inspired general intelligence. National Science Review, DOI:10.1093/nsr/nwad267.

[4] Yuxiang Sun et al. (2023). Self Generated Wargame AI: Double Layer Agent Task Planning Based on Large Language Model. arXiv preprint arXiv:2312.01090, DOI:10.48550/arXiv.2312.01090.

[5] Lixiang Yan et al. (2023). Practical and ethical challenges of large language models in education: A systematic scoping review. British Journal of Educational Technology, DOI:10.1111/bjet.13370.

[6] Carlin Soos and Levon Haroutunian (2024). On the Question of Authorship in Large Language Models. Journal of Literary Theory, DOI:10.5771/0943-7444-2024-2-83.

[7] Tuan Bui et al. (2024). Cross-Data Knowledge Graph Construction for LLM-enabled Educational Question-Answering System: A Case Study at HCMUT. arXiv preprint arXiv:2404.09296, arXiv:2404.09296.

[8] Yakun Wang and Fuxiang Yu (2023). Visualization and Analysis of Mapping Knowledge Domains for AI Education System Studies. 2023 IEEE 12th International Conference on Intelligent Education Technology (ICIET), DOI:10.1109/ICIET56899.2023.10111115.

[9] Krzysztof Edyko et al. (2023). Utilizing Artificial Intelligence Tools Using the GPT Chatbot in Medicine - A Review of Flaws, Advantages, and Limitations. Journal of Education, Health and Sport, DOI:10.12775/jehs.2023.46.01.008.

[10] Langchainchatchat, github.com/chatchat-space/Langchain-Chatchat/ wiki/

[11] LangChain, python.langchain.com/docs/get_started/introduction/ Streamlit Documentation, docs.streamlit.io/

[12] BAAI/Bge-Large-Zh-v1.5, Hugging Face, https://huggingface.co/BAAI/bge-large-zh-v1.5