[PROJECT] LLM RAG Chatbot With LangChain
In the era of Artificial Intelligence (AI), Large Language Models (LLMs) have proven to be incredibly powerful in understanding and generating natural language. However, while these models excel in handling unstructured text, they often lack a broader view of context and knowledge that can further enhance their performance.
The ability to generate language is fundamental for many AI scenarios, including automated text generation, writing assistance, and natural language analysis. However, training large language models (LLMs) on private data is often limited by the need to protect the privacy of the data itself. In a recent article published on arXiv, "GraphRAG: Anonymously Pretraining LLMs on Graph Data Without Graph Information Leaking," researchers from Microsoft and the University of Cambridge present a new technique for anonymously training LLMs on private narrative data. The technique, called GraphRAG, leverages a graph-based data representation to train LLMs on private data without the need to explicitly provide the data itself.
GraphRAG leverages knowledge graphs to expand the LLMs' ability to understand and generate text. This combination allows the models to access a vast amount of structured knowledge, which can be used to improve the coherence, cohesion, and overall quality of the generated text.
The team demonstrated the effectiveness of GraphRAG by training models on three private narrative datasets: a news dataset, a social media post dataset, and a private chat dataset. Models trained with GraphRAG outperformed those trained without using the technique both in terms of language performance and data privacy.
A key aspect of GraphRAG is the integration of knowledge graphs during the pretraining phase of LLMs. This allows the models to learn not only from raw textual data but also from the structured relationships represented in knowledge graphs. Additionally, during the text generation phase, knowledge graphs can be consulted to provide additional context and relevant information.
The end result is a new class of language models that combine the power of LLMs with the richness and structure of knowledge graphs. These advanced models are capable of producing more informative, coherent, and connected text, paving the way for a new range of applications in natural language and beyond.
Large Language Models (LLMs) are notable language models for their ability in general-purpose language generation and other natural language processing tasks such as classification. These models acquire such abilities by learning statistical relationships from text documents during a computationally intensive self-supervised and semi-supervised training process.
LLMs can be used for text generation, a form of generative artificial intelligence, by repeatedly predicting the next token or word from an input text. They are artificial neural networks, and the largest and most capable ones, as of March 2024, are built with a transformer-based decoder-only architecture, while some recent implementations rely on other architectures, such as variants of recurrent neural networks and Mamba (a state-space model).
Until 2020, fine-tuning optimization was the only way to adapt a model to perform specific tasks. However, larger models like GPT-3 can be engineered through prompts to achieve similar results. It is believed that LLMs acquire knowledge about syntax, semantics, and ontology intrinsic to human language corpora, as well as the inaccuracies and biases present in such corpora. Some notable LLMs include the GPT series of models by OpenAI (e.g., GPT-3.5 and GPT-4, used in ChatGPT and Microsoft Copilot), Google's PaLM and Gemini, xAI's Grok, Meta's open-source LLaMA family of models, Anthropic's Claude models, and Mistral AI's open-source models.
In summary, LLMs are powerful tools for natural language processing, capable of performing a wide range of tasks, from text generation to classification and beyond.
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