Retrieval Augmented Generation (RAG) techniques help reduce hallucinations in large language models (LLMs). RAG retrieves text based on semantic similarity, though it may not directly answer complex queries where specific details aren't explicitly mentioned in the dataset.

Knowledge Graphs (KGs) offer structured and explicit representations of knowledge, enhancing the reasoning capabilities of LLMs. However, creating and maintaining KGs required significant human effort and domain knowledge, which poses challenges for scaling and usage. Here, we evaluate the automatic creation of KGs from web pages.

Previously, utilizing KGs required proficiency in graph query languages such as Cypher, Gremlin, SPARQL, or RDF. This is no longer necessary, as LLMs can now generate the required queries with the correct schema and process the query results, providing users with a natural language interface to interact with KGs.

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AI assistants need access to high-quality corporate data to effectively automate routine business tasks. This requirement applies to data used for training, fine-tuning, and inference in Retrieval-Augmented Generation (RAG). 

A large amount of work still remains, including data labeling

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It verifies facts before presenting them to the user. It will work with the latest information and double-check it against relevant sources such as wikipedia or published papers.

If it generates ideas or concepts from other documents, It maintains a citation list and verifies primary sources to avoid mistakes.

It adheres to the theme and writing style of generated content requested by the user. 

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