For a more detailed information, please see content at Track 03 - Section 1.1

https://sites.google.com/view/statistics-on-customs/in%C3%ADcio/track03/descriptive-and-inference-statistics

A simple and short definition

A Large Language Model (LLM) is a language model that demonstrates remarkable versatility, excelling at a variety of Natural Language Processing (NLP) tasks. These tasks include summarization, extracting relevant information from large documents, answering questions, and creating narratives. Characteristics and Purposes of LLMs:

• LLMs integrate external knowledge sources during inference to provide accurate and up-to-date information.

• Retrieval-Augmented Generation (RAG) models augment language models by incorporating verifiable information, which improves factual accuracy in the generated answers.

• They can be transformed into experts by integrating a domain-specific knowledge base, enabling the development of highly targeted applications.

The next figure presents a conceptual landscape of the Large Language Models (LLMs) ecosystem, illustrating various categories of tools, models, and frameworks related to LLM development and usage [1].

Key components for LLMs

Large Language Models (LLMs) (Orange Circle):

This core area includes models focused on fundamental capabilities such as:

• Text Generation

• Classification

• Knowledge Answering

• Dialog Generation

• Translation

Examples of LLMs and Organizations (Green Area):

These are implementations or providers offering APIs and models for various LLM tasks. Models and platforms such as:

• OpenAI

• AI21 Labs

• GooseAI / EleutherAI

• Meta’s NLLB, BlenderBot, Sphere

• Google’s LaMDA

• BLOOM (BigScience)

• Cohere

• DialoGPT

• GODEL

Surrounding Ecosystem (Light Blue Area): 

This area includes tools and services built around LLMs:

Data-Centric Tooling: Tools like HumanFirst for dataset management, curation, and labeling.

Hosting: Platforms like Hugging Face, which provide model repositories, APIs, and deployment services.

Playgrounds & Prompt Engineering: Interfaces for testing prompts and models (e.g., console environments, APIs).

Notebooks: Platforms like Jupyter Notebooks, enabling interactive development, experimentation, and integration with LLM APIs.

Applications of LLMs

• Chatbots: LLMs are the basis for chatbots capable of maintaining natural conversations with users.

• Machine translation: Improves the quality and fluidity of machine translations.

• Text generation: Allows the creation of articles, summaries, scripts and other textual content.

• Text summarization: Helps to condense important information from long documents.

• Sentiment analysis: Allows the identification of emotional tone in texts.

• Research and development: Contributes to AI research and the creation of new applications.

Creating chatbots like ChatGPT

It is possible to create a chatbot based on python code in the Google Colab environment. This chatbot could be deployed as an app (streamlit platform) as illustrated in the next figure. The corresponding necessary code is given and explained in the reference [2].

An interesting application for chatbots could be to process several types of documents to understand and navigate through the material and estabilish connections among the materials. For this purpose, it is important to define another concept:  Retrieval Augmented Generation.

Retrieval Augmented Generation (RAG)

A Retrieval-Augmented Generation (RAG) combines the power of Large Language Models (LLMs) with external knowledge sources to generate more accurate and informative answers. LLMs are powerful in generating coherent and contextually relevant text, but they can struggle to provide accurate, up-to-date, and domain-specific information since they rely on static training data [4]. RAG addresses these limitations through a retrieval mechanism that allows the model to access external databases or knowledge sources during inference time. Advantages of RAG over LLMs [3]:

• Access to up-to-date information: RAG allows the model to access up-to-date, domain-specific information without the need for retraining. This is particularly important in contexts where course materials may be updated frequently.

• Reducing hallucinations: RAG reduces the risk of the model generating inaccurate or contrived answers (“hallucinations”). By basing answers on verified information from the knowledge base, RAG significantly reduces the likelihood of generating misleading answers compared to standard LLMs.

• Improving quality and accuracy: By leveraging large amounts of structured and unstructured data, RAG offers the potential to improve the quality and accuracy of language model outputs, bridging the gap between language generation and real-world knowledge.

• Ability to provide more specific and factual responses: Combining pre-trained parametric memories (LLMs) with non-parametric memory, such as a vector database, in RAG models provides a method for giving LLMs access to up-to-date information without retraining, enabling more specific and factual responses. Improving accuracy in specific contexts: Optimizing RAG retrieval components can make open LLMs perform comparably to private solutions on healthcare benchmarks, answering multiple-choice questions and generating more reliable open-ended responses.

The next figure gives a flowchart of how RAG works [5]:

It is possible to adapt RAG to deploy for real-time applications as show in the next figure [6]:

NotebookLM: a simple way to employ RAGs for Knowledge management

NotebookLM is a Google notebook with artificial intelligence (AI) at its core, designed to aid learning and research by allowing you to organize and synthesize information from multiple sources. NotebookLM can be related to RAG (Retrieval-Augmented Generation) because it uses advanced language models to become an expert on the documents provided, allowing you to [7]:

• Quickly summarize complex documents.

• Answer specific questions based on the source material.

• Turn documents into briefings, study guides, or podcasts.

• Connect ideas spread across multiple sources.

NotebookLM answers questions based on uploaded documents, providing quotes and excerpts from those documents. How NotebookLM relates to RAG [8, 9]:

• NotebookLM uses an architecture that resembles a RAG system, where documents are searched and retrieved based on their semantic relevance to a query and then passed to an LLM.

• NotebookLM can help students synthesize large amounts of information from multiple sources by allowing them to upload their primary research documents and ask targeted questions to quickly gain insights.

• NotebookLM enables educators to create interactive assignments, enhance lessons with contextual insights, and create interdisciplinary exercises that encourage students to view topics from different perspectives. Educators can upload lecture notes, research articles, and even recorded class sessions to NotebookLM to generate study guides, glossaries, and FAQs to support student understanding.

The next subsection will explain how to apply NotebookLM tool for the materials of this site.

References