Australian National University

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.

Introduction to APOC: Enhancing Neo4j Capabilities

Xuzeng He

Latest findings in multiple research directions for handling graph prediction and optimization Author · Xuzeng He (

 ORCID:

0009–0005–7317–7426) A graph, in short, is a description of items linked by relations, where the items of a graph are called

 nodes

(or vertices) and their relations are called

 edges

(or links). Examples of graphs can include social networks (e.g. Instagram) or knowledge

Recent Advances in using Machine Learning with Graphs

Vaibhav Khobragade

Author: Vaibhav Khobragade (

 ORCID:

0009–0009–8807–5982) Introduction Large language models (LLMs) are becoming increasingly popular in natural language processing for their superior competence in various applications.

Enhancing Language Models: The Role of Knowledge Graph Augmentation in Overcoming LLM Challenges

Enhancing Data Interactivity with LLMs and Neo4j Knowledge Graphs Author Wenyi Pi (

 ORCID

: 0009–0002–2884–2771) Introduction Since OpenAI launched ChatGPT, a

 large language model (LLM)

based chatbot, in 2023, it has set off a technological wave.

The integration of large language models (LLMs) with Neo4j-based knowledge graphs

An Introduction to RA-CM3, MuRAG and RACE Author  Xuzeng He (ORCID: 0009-0005-7317-7426)  Generative Artificial Intelligence (GAI) has demonstrated impressive performances in tasks such as text generation and text-to-image generation.

Recent Advances in using Retrieving Multimodal Information for Augmented Generation

Amanda Kau

Unlocking the Power of Questions — A deep dive into Question Answering Systems

Author:

 Amanda Kau (ORCID:


 0009–0004–4949–9284


 )

Virtual assistants have popped up on numerous websites over the years.

Harnessing The Power of Knowledge Graphs in Question Answering

Qingqin Fang

An Introduction to Retrieval Augmented Generation (RAG) and Knowledge Graph

Author Qingqin Fang (ORCID: 0009–0003–5348–4264)

 Introduction

Large Language Models (LLMs) have transformed the landscape of natural language processing, demonstrating exceptional proficiency in generating text that closely resembles human language.

Hallucination in Large Language Models and Two Effective Alleviation Pathways

Yunzhong Zhang

Efficient creation of a stoplight report with data dashboard images

Author: Yunzhong Zhang (ORCID: 0009–0002–8177–419X) Comparing data dashboards is crucial for understanding trends and performance differences. Traditionally, this task required manual effort, which was slow and sometimes inaccurate. Now, thanks to OpenAI’s GPT-4 with Vision (GPT-4V), we are able to automate and improve this process.

How to use GPT-4V For Stoplight Report

Dhruv Gupta

Improving the performance of Large Language Models Author  Dhruv Gupta (ORCID: 0009-0004-7109-5403)  ChatGPT, which first came out in late 2022, took the world by storm. Since then, various LLM models and LLM based products such as Meta’s Llama and Google’s Gemini have emerged, demonstrating the power of LLMs.

RAG: The next big thing after LLMs?

Harvesting the Power and Knowledge of Large Language Models


 Author

Amanda Kau

 (ORCID:

0009–0004–4949–9284

 )

Introduction Knowledge Graphs are networks that represent data in a graphical format. The beauty of Knowledge Graphs lies in their representation of concepts, events and entities as nodes, and the relationships between them as edges.

Automated Knowledge Graph Construction with Large Language Models

Swinburne University of Technology

Nakul Nambiar

Zhuochen Wu

Authors:  Nakul Nambiar (ORCID: 0009-0009-9720-9233) Zhuochen Wu (ORCID: 0009-0000-5642-5348)  Research Graph is a structured representation of research objects that captures information about entities and the relationships between Researcher, Organisation, Publication, Grant and Research Data.

How to use GPT API to export a research graph from PDF publications

Dhruv Gupta

Prompt Engineering — Part 2


 Using intelligence to use artificial Intelligence: A deep dive into Prompt Engineering

Author Dhruv Gupta

 (ORCID:

0009–0004–7109–5403

 )

Introduction In the previous article we discussed what prompt engineering and some of the techniques used for prompt engineering.

Prompt Engineering — Part 2

Enhancing Open-Domain Conversational Question Answering with Knowledge-Enhanced Models and Knowledge Graphs


 How knowledge-enhanced language models and knowledge graphs are advancing open-domain conversational question answering

Author:

 Wenyi Pi (ORCID:


 0009-0002-2884-2771


 )

When searching for information on the website, it is common to come across a flood of

Enhancing Open-Domain Conversational Question Answering with Knowledge-Enhanced Models and…

Recent Advances in Using Machine Learning with Graphs — Part 2 Latest findings in multiple research directions for handling graph construction and network security issues Author · Xuzeng He (

 ORCID:

0009–0005–7317–7426) Introduction A graph, in short, is a description of items linked by relations, where the items of a graph are called nodes (or vertices) and their relations are called edges (or links). Examples of

Recent Advances in Using Machine Learning with Graphs — Part 2

Exploring AI’s Ethical Terrain: Addressing Bias, Security, and Beyond

Author: Vaibhav Khobragade (

 ORCID:

0009–0009–8807–5982) Large language models (LLMs) like OpenAI’s GPT-4, Meta’s LLaMA, and Google Gemini (previously called Bard) have showcased their vast capabilities, from passing bar exams and crafting articles to generating images and website code.

Ethics and AI: Confronting the Challenges Ahead

How to efficiently retrieve information for different applications

Author Wenyi Pi (ORCID: 0009-0002-2884-2771) This article aims to explore various ways in which Retrieval-Augmented Generation (RAG) can be utilised to retrieve information and generate responses effectively within the dialogue system. The rationale behind utilising RAG as well as potential ways in which it can be employed effectively will be covered.

Efficient Information Retrieval and Response Generation with Retrieval-Augmented Generation (RAG)

Amir Aryani

Authors  Nakul Nambiar (ORCID: 0009-0009-9720-9233) Amir Aryani (ORCID: 0000-0002-4259-9774)  Knowledge graphs, which offer a structured representation of data and its relationships, are revolutionising how we organise and access information.

Mini RAG using Neo4j

Aishwarya Nambissan

A novel approach to improving the efficiency of text search in graph databases utilizing Neo4j, OpenAI, and Typesense. Authors  Nakul Nambiar (ORCID: 0009–0009–9720–9233) Aishwarya Nambissan (ORCID: 0009–0003–3823–6609)  The ability to use cutting-edge tools and frameworks is essential for staying ahead in the ever-changing field of technology.

Typesense and Neo4j in a hybrid information retrieval solution

Amir Aryani

A brief overview of different types of clustering techniques and their algorithms. Authors Aishwarya Nambissan (ORCID: 0009-0003-3823-6609) Amir Aryani (ORCID: 0000-0002-4259-9774)

 Background

Clustering is a fascinating technique used in machine learning, where patterns or data points are grouped based on their similarities. It’s like finding hidden connections among different data points without predefined labels.

19 Clustering Techniques

Understanding the Evolutionary Journey of LLMs Author Wenyi Pi (

 ORCID

: 0009–0002–2884–2771) Introduction When we talk about large language models (LLMs), we are actually referring to a type of advanced software that can communicate in a human-like manner. These models have the amazing ability to understand complex contexts and generate content that is coherent and has a human feel.

Brief Introduction to the History of Large Language Models (LLMs)

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

Beginner’s Guide to Recurrent Neural Networks (RNNs) with Keras

Incorporating Knowledge Graphs to explain reasoning processes


 Author

Amanda Kau (

 ORCID:

0009–0004–4949–9284) Introduction Large language models (LLMs) like GPT-4 possess remarkable language abilities, allowing them to function as chatbots, translators, and much more.

Combining Knowledge Graphs With Language Models for Interpretability

Exploring the OpenAlex Data Structure and Visualization Author: Qingqin Fang (

 ORCID:

0009–0003–5348–4264)

 Introduction to OpenAlex

In today’s world, the realm of research papers is brimming with countless hot topics, and the sheer volume of publications can be overwhelming.

Unveiling Research Trends through OpenAlex Visualization

Large Language Models for Fake News Generation and Detection Author Amanda Kau (

 ORCID

: 0009–0004–4949–9284) Introduction In recent years, fake news has become an increasing concern for many, and for good reason. Newspapers, which we once trusted to deliver credible news through accountable journalists, are vanishing en masse along with their writers.

Are Large Language Models Our Allies or Enemies in the Fight Against Fake News?

Unlocking the power of language models: A deep dive into BERT


 Author:


 Dhruv Gupta (ORCID:


 0009–0004–7109–5403


 )

Clive Humby, in 2006 rightly said, “Data is the new oil”. With data being present everywhere, it has never been more valuable.

Language Models: Deep Dive into BERT

Attention mechanism not getting enough attention Author Dhruv Gupta (

 ORCID

: 0009–0004–7109–5403) Introduction As discussed in this article, RNNs were incapable of learning long-term dependencies. To solve this issue both LSTMs and GRUs were introduced. However, even though LSTMs and GRUs did a fairly decent job for textual data they did not perform well.

Transformers Models in NLP

Exploring the potentials and limitations of Vision Language Models

Author:

 Amanda Kau (ORCID:


 0009–0004–4949–9284


 )

The human brain is more extraordinary than any machine we could build. From an early age, many of us gain the ability to comprehend what our eyes tell us and articulate it. Furthermore, we combine evidence from all our senses to reason.

How Much Can Vision Language Models Really “See”?

A novel compression technique ensuring comparable performance with 70% less parameters

Author Amanda Kau (

 ORCID

: 0009–0004–4949–9284) Introduction The sizes of large language models (LLMs) have been steadily increasing over the last few years.

CompactifAI: Large Language Models Don’t Actually Have To Be Large

From Naive to Modular: Tracing the Evolution of Retrieval-Augmented Generation

Author · Vaibhav Khobragade (

 ORCID:

0009–0009–8807–5982) Introduction Large Language Models (LLMs) have achieved remarkable success.

Three Paradigms of RAG

An introduction to tools supporting PubMed Author: Xuzeng He (

 ORCID:

0009–0005–7317–7426) PubMed is a free Web literature search service developed and maintained by the National Center for Biotechnology Information (

 NCBI

), and it is also a part of NCBI’s Entrez retrieval system that provides access to a diverse set of 38 databases.

Enhancing PubMed: from a Medical Database to beyond

Aland Astudillo

How to use GROBID to extract text from PDF  Author  Aland Astudillo (ORCID: 0009-0008-8672-3168)  GROBID is a powerful and useful tool based on machine learning that can extract text information from PDF files and other files to a structured format. One of the key challenges in knowledge mining from academic articles is reading the content of PDF files.

How to use GROBID

Using Mistral for Data tagging Author · Xuzeng He (

 ORCID:

0009–0005–7317–7426) Introduction Data tagging, in simple terms, is the process of assigning labels or tags to your data so that they are easier to retrieve or analyse.

How to use Large Language Models to tag your data: A complete tutorial

Integrating temporal data into static knowledge graphs

 Author

Amanda Kau

 (ORCID:


 0009–0004–4949–9284


 )

Introduction Knowledge graphs (KGs) have proven to be an effective method of data representation that is increasingly popular. In KGs, entities and concepts are represented as nodes, while the relationships between nodes are depicted as edges.

Dynamic Knowledge Graphs: A Next Step For Data Representation?

Transformative Advances in Language Models through External Knowledge Integration

Author: Qingqin Fang (

 ORCID:

0009–0003–5348–4264)

 Introduction

In the dynamic field of natural language processing, the integration of external knowledge has emerged as a pivotal strategy for enhancing the performance of language models.

Deeper understanding of In-Context Retrieval-Augmented Language Models

Author  Amir Aryani (ORCID: 0000-0002-4259-9774) Introduction   In this article we look at Research Graph as an information model , and an approach to connect and capture the connections between research outputs, researchers and research activities. We explore the metadata model, and we discuss how to capture this graph in a Neo4j Graph Database.

Research Graph 101

Latest findings in multiple research directions for tackling reasoning and common sense challenges Author: Xuzeng He (

 ORCID:

0009–0005–7317–7426) Knowledge Graphs, such as Wikidata, contain rich relational information between entities and have been widely used as a structured format for storing and representing relational information.

Large Language Models and Knowledge Graphs: Ways to combine them

Using intelligence to use artificial Intelligence: A deep dive into Prompt Engineering


 Author


 Dhruv Gupta (ORCID:


 0009–0004–7109–5403


 )

Introduction Large Language Models (LLMs) have become the new normal in the field of Natural Language Processing (NLP). With their improved performance and generative power, people around the world are relying on it for

Prompt Engineering

Boosting Performance for Knowledge Graphs with Neo4j APOC Library

 Author

Wenyi Pi (ORCID: 0009–0002–2884–2771) Introduction A

 knowledge graph

(graph database) captures information about main entities in a domain and the relationships between them. It was an augmented feature store for connected data which gave access to compute, access and operationalise structure features.

Exploring Methods of Cypher Query Optimisations

The AI Helper Turning Mountains of Data into Bite-Sized Instructions

Author Aland Astudillo (ORCID: 0009-0008-8672-3168) LLMs have been changing the way the entire world deals with problems and day-by-day tasks. To make them better for specific applications, they need huge amounts of data and complex and expensive approaches to training them.

What is LLMLingua?

Refining AI Vision: How Retrieval-Augmented Generation Transforms Image Captioning in Large Language Models   Leveraging External Knowledge to Enhance the Descriptive Capabilities of AI Systems Author  Vaibhav Khobragade (ORCID: 0009–0009–8807–5982) Introduction   Large Language Models (LLMs) are artificial intelligence models that are trained on massive amounts of text data in order to generate human-like language and produce coherent

Refining AI Vision: How Retrieval-Augmented Generation Transforms Image Captioning in Large…

Tools and Platform for Integration of Knowledge Graph with RAG pipelines.

Authors Aland Astudillo (ORCID: 0009-0008-8672-3168) Aishwarya Nambissan (ORCID: 0009-0003-3823-6609) Many users of chatbots such as ChatGPT, have encountered the problem of receiving inappropriate or incompatible responses. There are several reasons why this might happen.

Unveiling the Synergy: Retrieval Augmented Generation (RAG) Meets Knowledge Graphs

Author  Amir Aryani: (ORCID: 0000-0002-4259-9774) Definition   A research collaboration network is a group of researchers, and practitioners, or both, working together on joint research activities. These networks often span across disciplines, geographic boundaries, and sectors, enabling participants to share resources, expertise, and data to address common research goals more effectively than they could individually.

Research Collaboration Network

Improving the performance and application of Large Language Models

Author Amanda Kau (ORCID: 0009-0004-4949-9284) Large language models (LLMs) like GPT-4, the engine of products like ChatGPT, have taken centre stage in recent years due to their astonishing capabilities. Yet, they are far from perfect.

Understanding Retrieval Pitfalls: Challenges Faced by Retrieval Augmented Generation (RAG) models

Exploring the Potential of Temporal Feature-Logic Embedding (TFLEX) in Complex Query Resolution

Author · Vaibhav Khobragade (

 ORCID:

0009–0009–8807–5982)

 Introduction

Artificial intelligence (AI) and knowledge representation in the field of temporal knowledge graphs are rapidly gaining interest.

Harnessing Temporal Dynamics: Advanced Reasoning using Temporal Knowledge Graphs

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.

Multimodal Large Language Models for Misinformation Detection and Reasoning

The Three Oldest Pillars of NLP

Author Dhruv Gupta (

 ORCID

: 0009–0004–7109–5403) Introduction Natural Language Processing (NLP) has almost become synonymous with Large Language Models (LLMs), Generative AI, and fancy chatbots. With the ever-increasing amount of textual data and exponential growth in computational knowledge, these models are improving every day.

RNNs vs GRUs vs LSTMs

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.

RAG 2.0 is Coming?

Bridging Human Perception and AI’s Future: The Convergence of Visual Understanding and Semantic Networks Author · Vaibhav Khobragade (

 ORCID:

0009–0009–8807–5982)

 Introduction

The fusion of Vision-Language Models (

 VLMs

), Generative Models, and Knowledge Graphs (

 KGs

) is reshaping how artificial intelligence (AI) understands and interacts with the world.

Tuning Vision-Language Models and Generative Models with Knowledge Graph

Understanding the Power and Applications of Natural Language Processing

 Author

Dhruv Gupta (

 ORCID:

0009–0004–7109–5403) Introduction We are living in the era of generative AI. In an era where you can ask AI models almost anything, they will most certainly have an answer to the query. With the increased computational power and the amount of textual data, these models are bound to improve their performance.

NLU vs. NLG: Unveiling the Two Sides of Natural Language Processing

Understanding how RNNs work and its applications


 Author

Wenyi Pi (

 ORCID

: 0009–0002–2884–2771) Introduction In the ever-evolving landscape of artificial intelligence (AI), bridging the gap between humans and machines has seen remarkable progress. Researchers and enthusiasts alike have tirelessly worked across numerous aspects of this field, bringing about amazing advancements.

An Introduction to Recurrent Neural Networks (RNNs)

Exploring the Boundaries of Creativity and Responsibility in the Age of AI-Driven Media

 Author

Vaibhav Khobragade (

 ORCID:

0009–0009–8807–5982)

 Introduction

In 2024, the discipline of Generative AI takes a big step forward with the launch of revolutionary models that convert text into dynamic films, altering the landscape of digital content creation.

Generative AI’s Leap into Video in 2024 and its Ethical Horizon

Addressing Misinformation, Bias, and Privacy Concerns

Author:

 Wenyi Pi (ORCID:


 0009–0002–2884–2771


 )

Recently, the spread of false or misinformation has become an important concern for people all over the world, especially in the era of big data and multimedia, where people are faced with a flood of information that makes it difficult to determine its authenticity.

The Ethical and Social Impact of Artificial Intelligence

Latest findings for the use of Knowledge Graph in the field of QA in multiple research directions Author: Xuzeng He (

 ORCID:

0009–0005–7317–7426) Question Answering (QA), the ability to interact with data using natural language questions and obtaining accurate results, has been a long-standing challenge in computer science dating back to the 1960s.

Question Answering enhanced by Knowledge Graph

Unlocking the power of knowledge graphs in research catalogues: A deep dive into OpenAlex


 Author:


 Dhruv Gupta (ORCID:


 0009–0004–7109–5403


 )

Clive Humby, in 2006 rightly said, “Data is the new oil”. With data being present everywhere, it has never been more valuable.

Knowledge Graphs: Deep Dive into OpenAlex

Techniques to integrate Knowledge Graphs into Language Models

 Author

Amanda Kau

 (ORCID:


 0009–0004–4949–9284


 )

Introduction Both knowledge graphs (KGs) and pre-trained language models (PLMs) have gained popularity due to their ability to comprehend world knowledge and their broad applicability.

A Deep Dive Into Knowledge Graph Enhanced Pre-trained Language Models

Automated Knowledge Graph Construction with Large Language Models — Part 2

 Harvesting the Power and Knowledge of Large Language Models

Author Amanda Kau (

 ORCID

:

 0009–0004–4949–9284

) Introduction Knowledge graphs (KGs) are a structured representation of data in a graphical format, in which entities are represented by nodes and are connected by edges representing relationships

Automated Knowledge Graph Construction with Large Language Models — Part 2

An improvement architecture superior to the Transformer, proposed by Meta Author · Qingqin Fang (

 ORCID:

0009–0003–5348–4264) Introduction Recently, researchers from Meta and the University of Southern California have introduced a model called Megalodon. They claim that this model can expand the context window of language models to handle millions of tokens without overwhelming your memory.

The longer the context, the better? Unlimited Context Length in Megalodon

Latest findings in pre-training graphs and using them for link recommendation Author · Xuzeng He (

 ORCID:

0009–0005–7317–7426) Introduction A graph, in short, is a description of items linked by relations, where the items of a graph are called nodes (or vertices) and their relations are called edges (or links). Examples of graphs can include social networks (e.g. Instagram) or knowledge graphs (e.g. Wikipedia). In Instagram

Working with Graphs: Pre-training and Application

Unlocking the Future of AI: The Transformative Journey of Large Language Models Author · Vaibhav Khobragade (

 ORCID:

0009–0009–8807–5982)

 Introduction

Human language development is innate and evolves throughout life. Machines lack this ability to evolve without advanced AI algorithms.

The Journey of Large Language Models: Evolution, Application, and Limitations

Understanding Knowledge Networks From A Graph Perspective


 Author:


 Amanda Kau (ORCID:


 0009–0004–4949–9284


 )

Since 2020, over ten million scholarly articles have been published annually. To put that into perspective, say all ten million articles were released on the first day of the year.

Uncovering The Secrets Behind The Most Influential Scholarly Publications With AceMap

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.

When RAG and LLM Conflict: Who Will AI Listen to?

Solutions to Enhance LLM Performance in Long Contexts Author · Qingqin Fang (

 ORCID:

0009–0003–5348–4264) Introduction In the era of AI breakthroughs, large language models (LLMs) are not just advancements; they are revolutions, transforming how we interact with technology, from casual conversations with chatbots to the intricate mechanisms behind sophisticated data analysis tools.

Navigating the Long Context Conundrum: Challenges in Language Models’ Information Processing

Supervised Fine-tuning, Reinforcement Learning from Human Feedback and the latest SteerLM Author · Xuzeng He (

 ORCID:

0009–0005–7317–7426) Introduction Large Language Models (LLMs), usually trained with extensive text data, can demonstrate remarkable capabilities in handling various tasks with state-of-the-art performance. However, people nowadays typically want something more personalised instead of a general solution.

Fine-tuning Large Language Models: A Brief Introduction

An Overview of Constructing a Knowledge Graph Author · Qingqin Fang (

 ORCID:

0009–0003–5348–4264) 1. Introduction 1.1 What is a Knowledge Graph Knowledge Graphs are structured semantic knowledge bases used to rapidly describe concepts and their relationships in the physical world.

Unlocking Intelligence: The Journey from Data to Knowledge Graph

Author Dhruv Gupta (

 ORCID

: 0009–0004–7109–5403) Introduction Large Language Models (LLMs) have become the new face of Natural language processing (NLP). With their generative power and ability to comprehend human language, the human reliance on these models is increasing every day. However, the LLMs have been known to hallucinate and thus produce wrong outputs.

Prompt engineering: A Way to Smartly Use AI

Latest effort in assessing the security of the code generated by large language models Author · Xuzeng He (

 ORCID:

0009–0005–7317–7426) Introduction With the surge of Large Language Models (LLMs) nowadays, there is a rising trend among developers to use Large Language Models to assist their daily code writing. Famous products include GitHub Copilot or simply ChatGPT.

Large Language Models for Code Writing: Security Assessment

Research Graph

Google’s Groundbreaking AI Model Explained
 


 Introduction

In the rapidly evolving landscape of artificial intelligence, Google continues to stand at the forefront, consistently pushing the boundaries of what these technologies can achieve. One of their latest advancements, GEMMA2, represents a significant leap in AI capabilities.

What is Gemma2?

Stories by Research Graph on Medium

#### **From Naive to Modular: Tracing the Evolution of Retrieval-Augmented Generation**

<figure>
<img src="https://cdn-images-1.medium.com/max/938/0*IQgdTaKG50F_nbGM" />
<figcaption>Created by DALLE</figcaption>
</figure>

### Author

· Vaibhav Khobragade (**ORCID:**
[0009--0009--8807--5982](https://orcid.org/0009-0009-8807-5982))

### Introduction

Large Language Models (LLMs) have achieved remarkable success. But, they
still face significant limitations, especially in domain-specific or
knowledge-intensive tasks such as question answering, producing
"hallucinations" where the models generate responses that sound
plausible but are actually incorrect when handling queries beyond their
training data or requiring current information.

To overcome these challenges, [Retrieval-Augmented Generation
(RAG)](https://medium.com/@researchgraph/refining-ai-vision-how-retrieval-augmented-generation-transforms-image-captioning-in-large-1a74499a370d)
enhances LLMs by retrieving relevant document chunks from external
knowledge bases through semantic similarity calculation. By referencing
external knowledge, RAG effectively reduces the problem of generating
factually incorrect content. Its integration into LLMs has resulted in
widespread adoption, establishing RAG as a key technology not just in
advancing chatbots, but also in vision language models to support
generating responses for real-world applications.

RAG instance when applied to question-answering tasks follows
this process:

<figure>
<img src="https://cdn-images-1.medium.com/max/940/0*rbHdvUc4YFFKs0SD" />
<figcaption>Figure 1: An example of how the Retrieval-Augmented
Generation (RAG) process is used for answering questions. (Gao et al.,
2023) Article link: <a
href="https://arxiv.org/abs/2312.10997">https://arxiv.org/abs/2312.10997</a></figcaption>
</figure>

The diagram consists of three main steps. First, documents are divided
into smaller parts, converted into vectors, and stored in a database.
Second, the system selects the top k most relevant chunks based on the
question's meaning. Third, the original question and selected chunks are
inputted into a large language model to produce the answer.

<figure>
<img
src="https://cdn-images-1.medium.com/max/1016/0*uEXFltuz-jl29heb" />
<figcaption>Figure 2. Technology tree of RAG research. The stages of
involving RAG mainly include pre-training, fine-tuning, and inference.
(Gao et al., 2023) Article link: <a
href="https://arxiv.org/abs/2312.10997">https://arxiv.org/abs/2312.10997</a></figcaption>
</figure>

RAG technology has rapidly developed in recent years, and the technology
tree summarizing related research is shown above in Figure 2. Initially,
RAGs emerged with Transformer architecture, focusing on enhancing
language models by incorporating additional knowledge through
PreTraining Models (PTM). This early stage was characterized by
foundational work aimed at refining pre-training techniques. The
subsequent arrival of ChatGPT marked a pivotal moment, with LLMs
demonstrating powerful in-context learning (ICL) capabilities. As
research progressed, the enhancement of RAG was no longer limited to the
inference stage but began to be incorporated more with LLM fine-tuning
techniques.

In this article, we review the state-of-the-art RAG method and describe
its evolution through paradigms including Naive RAG, Advanced RAG, and
Modular RAG.

### **Naive RAG**

This is the earliest method of RAG to get prominence shortly after the
advent of ChatGPT. The Naive RAG follows a traditional process that
includes indexing, retrieval, and generation.

Let's understand Naïve RAG with an example.

**Scenario:** Suppose a user asks an LLM, "What are the health benefits
of drinking green tea?"

**Without Naive RAG:** The LLM might generate a response based solely on
its pre-trained knowledge, which could be outdated or incomplete. It
might say, "Green tea is rich in antioxidants and can help in improving
brain function."

**With Naive RAG:**

1\. **Indexing:** It begins by cleaning and extracting raw data about
green tea from various formats like PDF, HTML, Word, and Markdown. This
data is converted into plain text for consistency. To address the
context limitations of language models, the text is divided into
smaller, manageable chunks. These chunks are then turned into vectors
using an embedding model and stored in a vector database. This step is
important for enabling quick and accurate searches in the retrieval
phase and providing up-to-date research and ***articles about green tea
are indexed.***

2\. **Retrieval:** When the RAG system gets a user question, it uses the
same encoding model as before to turn the question into a vector. Then,
it compares this vector to the vectors of the indexed text chunks. The
system picks the top K chunks that are most similar to the question.
These chunks become the extra context for generating the response.
***For example: It retrieves closely related chunks to "health benefits
of green tea."***

3\. **Generation:** The user question and chosen documents are combined
into a clear prompt. The model might answer differently based on
specific task rules. It can use its built-in knowledge or stick only to
the given documents. If it's part of a conversation, previous chat
history can be added to the prompt, helping the model engage better in
multi-turn dialogues. ***For instance, it might retrieve a recent study
stating, "Green tea consumption is linked to reduced risk of heart
disease," which wasn't part of its initial training data.***

<figure>
<img src="https://cdn-images-1.medium.com/max/346/0*uvgeymdjpJec0jk3" />
<figcaption>Figure 3 Naïve RAG Pipeline (Gao et al., 2023) Article link:
<a
href="https://arxiv.org/abs/2312.10997">https://arxiv.org/abs/2312.10997</a></figcaption>
</figure>

However, Naive RAG encounters notable drawbacks:

**Retrieval Challenges --- **that lead to the selection of misaligned or
irrelevant chunks, and the missing of crucial information.

**Generating Challenges --- **the model might struggle with
hallucination and have problems with relevance, toxicity, or bias in its
outputs, which can make the responses less reliable and of lower quality

For instance: The Naive RAG model retrieves relevant documents and
provides them to the LLM. However, the retrieved information may not be
relevant or up to date. ***"Recent studies suggest that new medications
and therapies are being developed to slow the progression of
Alzheimer's."***

### **Advanced RAG**

Advanced RAG introduces specific improvements to overcome the
limitations of Naive RAG. Focusing on enhancing retrieval quality, it
employs pre-retrieval and post-retrieval strategies.

<figure>
<img src="https://cdn-images-1.medium.com/max/375/0*1PrMV9yHEE1eJG0b" />
<figcaption>Figure 4. Advanced RAG Pipeline</figcaption>
</figure>

**Scenario**: Consider a user asking, "What recent advances have been
made in the treatment of Alzheimer's disease?"

**With Advanced RAG:** The following procedures are followed:

**Pre-retrieval process:** The main aim is to improve the indexing
structure and the user's query. Indexing optimization focuses on making
the indexed content better. This includes strategies like improving data
details, organizing indexes better, adding extra information, aligning
things correctly, and using mixed retrieval methods, where information
from external sources is combined with the indexed content for retrieval
purposes. Query optimization aims to make the user's original query
clearer and more suitable for retrieval. Common methods include query
rewriting and query transformation & expansion.

For instance: Using the Alzheimer's example, Advanced RAG use a refined
indexing strategy where documents are not only indexed by content but
also by recency and relevance to medical advancements, using metadata
enhancements. Utilizing enhanced query transformation techniques, the
system precisely retrieves the most current and relevant research papers
discussing the latest treatment breakthroughs, rather than broader
articles on Alzheimer's.

**Post-Retrieval Process:** After retrieving relevant information, it is
essential to effectively combine it with the original query. The main
steps include re-ranking to highlight the most important content and
compressing the context to avoid information overload as retrieved
documents are then re-ranked to prioritize those that specifically
mention recent clinical trials or FDA approvals, ensuring that the most
pertinent information is considered first.

**Context compression:** Redundant or less relevant information is
filtered out to focus on key findings from the most authoritative
sources.

So, the result could be: "The latest breakthrough in Alzheimer's
treatment includes the development of a new drug, Aducanumab, which has
shown potential in reducing cognitive decline in early-stage patients,
according to a 2021 FDA report."

### **Modular RAG**

The modular RAG architecture advances beyond the former two RAG
paradigms, offering enhanced adaptability and versatility. It
incorporates diverse strategies for improving its components, such as
adding a **search module** for similarity searches and refining the
retriever through **fine-tuning**. Modular RAG builds upon the
foundational principles of Advanced and Naive RAG, illustrating a
progression and refinement within the RAG family.

<figure>
<img src="https://cdn-images-1.medium.com/max/701/0*JBAhcxxaIuT8DI0m" />
<figcaption>Figure 5 Modular RAG Pipeline</figcaption>
</figure>

**Modules:** The Modular RAG framework introduces additional specialized
components to enhance retrieval and processing capabilities.

Consider the scenario: ***"What are the latest research findings on the
health benefits of green tea?"***

1. **Search Module:** When the question is posed, this module activates
 and uses LLM-generated queries to search through various databases
 and knowledge graphs specifically for the latest research papers or
 articles on green tea.
2. **RAG-Fusion:** This module takes the initial user query and expands
 it into multiple queries. For instance, it might generate additional
 queries like "recent clinical trials on green tea" and "green tea
 effects on mental health." These expanded queries are then used to
 search for a wide range of relevant information.
3. **Memory Module:** As articles and papers are retrieved, this module
 helps by recalling previously retrieved documents on similar topics
 to improve the relevance and depth of the current search, using the
 system's built-in memory of past queries and their results.
4. **Routing:** This module analyzes the incoming data and decides the
 best path for processing it. For example, it might route the query
 to specific scientific databases for academic papers rather than
 general websites.
5. **Predict Module:** To ensure the retrieved information isn't
 redundant and is relevant, this module generates a synthesized
 context from the retrieved data, focusing only on the most pertinent
 information like key findings or new insights about green tea.
6. **Task Adapter Module:** Tailors RAG to specific tasks by automating
 prompt retrieval for zero-shot inputs and creating task-specific
 retrievers. For example, this module recognizes that the query is
 about health benefits, a specific knowledge domain. It adjusts the
 retrieval process to focus on the most credible medical and
 health-oriented sources and might generate specialized prompts to
 refine further retrieval.

#### **Patterns**

RAG uses various flexible and interchangeable modules to enhance the
performance of LLMs as follows:

1. **Module Interaction and Adjustment:** These are methods in Modular
 RAG that show how different parts (modules) of the system work
 together more dynamically. DSP (Demonstrate-Search-Predict) and
 ITERRETGEN (Iterative Retrieve-Read-Retrieve-Read) use the outputs
 from one module to improve or inform the function of another,
 enhancing the overall synergy and effectiveness. Example Patterns:
 [**DSP**](https://doi.org/10.48550/arXiv.2212.14024) and
 [**ITERRETGEN**](https://doi.org/10.48550/arXiv.2305.15294)
2. **Flexible Orchestration:** These methods demonstrate the
 adaptability of Modular RAG, where the system can adjust its
 retrieval processes based on what's needed for a specific scenario.
 This flexibility means the system isn't stuck to a fixed way of
 retrieving information but can adapt based on the requirements of
 the task at hand. Example Techniques**:**
 [**FLARE**](https://arxiv.org/pdf/2312.10997)
 and [**Self-RAG**](https://doi.org/10.48550/arXiv.2310.11511)
3. **Integration with Other Technologies:** Modular RAG can also easily
 work with other advanced technologies. For example, it can improve
 retrieval accuracy by fine-tuning the retrieval part of the system,
 personalize outputs by fine-tuning the generation process or even
 use reinforcement learning to enhance both processes
 collaboratively. Examples are **Fine-Tuning** and **Reinforcement
 Learning:**

### **Comparison of RAG with other model optimization methods**

<figure>
<img
src="https://cdn-images-1.medium.com/max/1022/0*vkYF69LpphrOyfhC" />
<figcaption>Figure 6 Compares Retrieval-Augmented Generation (RAG) with
other model optimization methods, specifically Fine-tuning and Prompt
Engineering.</figcaption>
</figure>

The diagram, presented as a quadrant chart, illustrates the differences
based on two dimensions: "External Knowledge Required" and "Model
Adaptation Required."

1. **Prompt Engineering**: Requires minimal model modifications and
 external knowledge, leveraging the inherent capabilities of LLMs.
2. **Fine-tuning (FT)**: Involves further training of the model to
 internalize knowledge, making it suitable for tasks that require
 specific structures, styles, or formats.
3. **RAG**: RAG typically requires less training of the model itself
 compared to methods like Fine-tuning. Instead, RAG heavily utilizes
 external, updated knowledge sources to enhance the model's output.
 This allows RAG to provide more accurate and current information by
 integrating the latest data from outside databases and texts,
 without the need for extensive retraining of the model.

### **Future Works**

The key future developments highlighted for RAG are as follows:

**Optimization of Retrieval Efficiency:** Future research can focus on
ways of accelerating the retrieval process more effectively for large
knowledge bases. Optimization of indexing and retrieval of data can be
done to reduce latency and increase the responsiveness to the maximum.

**Multi-Modal RAG:** Take RAG to the next level by making it work with
not only text but other modalities like images, audio, and video. Plenty
of innovations have come through for Multi-Modal RAG already, but more
research in this area will always be helpful.

**Operational and Production Efficiency:** Demonstrate and enhance the
practical deployment of RAG systems within the production environment
with improved management of computational resources, and data security
assured in commercial applications.

### **Conclusion**

This article discusses RAG, a method that enhances LLMs by integrating
external knowledge to improve response accuracy, especially for complex,
real-world applications. RAG reduces factual inaccuracies by accessing
up-to-date databases, supporting various RAG paradigms like Naive,
Advanced, and Modular RAG, each offering specific improvements and
flexibility for task-specific applications.

### **References**

- Gao, Y., Xiong, Y., Gao, X., Jia, K., Pan, J., Bi, Y., Dai, Y., Sun,
 J., Wang, M., & Wang, H. (2023, December 18). *Retrieval-Augmented
 Generation for Large Language Models: A Survey*. arXiv.org.
 <https://arxiv.org/abs/2312.10997>
- Peng, W., Li, G., Jiang, Y., Wang, Z., Ou, D., Zeng, X., Xu, D., Xu,
 T., & Chen, E. (2023, November 7). *Large Language Model based
 Long-tail Query Rewriting in Taobao Search*. arXiv.org.
 <https://arxiv.org/abs/2311.03758>
- Ma, X., Gong, Y., He, P., Zhao, H., & Duan, N. (2023, May 23). *Query
 Rewriting for Retrieval-Augmented Large Language Models*. arXiv.org.
 <https://arxiv.org/abs/2305.14283>

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