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Large-language-modelsFrameworkRetrieval-augmentedInformatikEnglisch
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A Unified and Collaborative Framework for LLM Author · Qingqin Fang ( ORCID: 0009–0003–5348–4264) Introduction In today’s rapidly evolving field of artificial intelligence, large language models (LLMs) are demonstrating unprecedented potential. Particularly, the Retrieval-Augmented Generation (RAG) architecture has become a hot topic in AI technology due to its unique technical capabilities.

Large-language-modelsArtificial-intelligenceRetrieval-augmented-genMisinformation-researchInformatikEnglisch
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Autor Wenyi Pi

Exploring innovative Strategies in Combating Misinformation with Enhanced Multimodal Understanding Author Wenyi Pi ( ORCID : 0009–0002–2884–2771) Introduction Misinformation refers to false or inaccurate information that is often given to someone in a deliberate attempt to make them believe something that is not true. This has a significantly negative impact on public health, political stability and social trust and harmony.

ConflictLarge-language-modelsRetrieval-augmented-genInformatikEnglisch
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Understanding the Balance between Internal Knowledge and External Sources Author Qingqin Fang ( ORCID: 0009–0003–5348–4264) Introduction Previous research often emphasized the limitations of LLM’s information acquisition pathways, focusing on enhancing its capabilities in this regard.

Neo4jApocCypherKnowledge-graphInformatikEnglisch
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Autor Wenyi Pi

Neo4j APOC Library Use Case Author Wenyi Pi ( ORCID: 0009–0002–2884–277) Introduction In the realm of Neo4j, the APOC (Awesome Procedures on Cypher) library stands as a powerful tool. Previously, We have talked about the importance of APOC in optimising Cypher queries and improving query efficiency in our article Exploring Methods of Cypher Query Optimisations.

TensorflowKerasRecurrent-neural-networkInformatikEnglisch
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Autor Wenyi Pi

Understanding Sequential Data Modelling with Keras for Time Series Prediction Author Wenyi Pi ( ORCID : 0009–0002–2884–2771) Introduction Recurrent Neural Networks (RNNs) are a special type of neural networks that are suitable for learning representations of sequential data like text in Natural Language Processing (NLP). We will walk through a complete example of using RNNs for time series prediction, covering