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

Stories by Research Graph on Medium

#### Understanding the Evolutionary Journey of LLMs

<figure>
<img
src="https://cdn-images-1.medium.com/max/1000/0*fpp2b149lgG3cmWF" />
<figcaption>Image generated in DALL-E 2.</figcaption>
</figure>

### Author

- Wenyi Pi (**ORCID**:
 [0009--0002--2884--2771](https://orcid.org/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.

If you've ever chatted with an AI chatbot or virtual assistant, you
might actually interact with a LLMs, probably without even realising it.
These models are used far beyond chatbots and have a wide range of
applications such as in text generation, automatic translation,
sentiment analysis, document summarisation, and many other scenarios!

LLMs have become an essential part of the artificial intelligence (AI)
landscape. In this article, we will delve into the world of LLMs,
exploring their history and the evolution of LLMs.

### What Is a Large Language Model?

Large Language Models (LLMs) refer to large, general-purpose language
processing models that are first pre-trained on extensive datasets
covering a wide range of topics to learn and master the fundamental
structures and semantics of human language. The term "large" in this
context denotes both the substantial amount of data required for
training and the billions or even trillions of parameters that the model
contains. Pre-training equips the model to handle common language tasks
such as text classification, question answering, and document
summarisation, demonstrating its versatility.

After pre-training, these models are typically fine-tuned for specific
applications, such as on smaller, specialised datasets targeted at
particular domains like finance or medical, to enhance accuracy and
efficiency in addressing specific issues. This approach of pre-training
followed by fine-tuning enables LLMs not only to solve a broad range of
general problems but also to adapt to specific application requirements.

### Evolution of Large Language Models

<figure>
<img
src="https://cdn-images-1.medium.com/max/1000/0*B5LPhdBHfCjoGQpk" />
<figcaption>Large language model (LLM) timeline. Source: <a
href="https://www.youtube.com/watch?v=K7o5_Fj7_SY">Brief History of
Large Language Models &amp; Generative AI | Evolution of NLP from Eliza
to ChatGPT</a></figcaption>
</figure>

The image above provides an overview of the timeline for LLMs. We will
discuss each important phase in detail in the following sections.

#### Early Days: chatbots and rule-based systems (1960s)

Way back in 1966, the world witnessed the birth of ELIZA, which is
considered to be the first chatbot ever built by humans. Created by
Joseph Weizenbaum at MIT, ELIZA was a groundbreaking experiment of its
time, enabling human-computer interaction. While it didn't understand
the conversation context like the way humans do or like ChatGPT does
nowadays, it can create an illusion of a conversation by rephrasing user
statements as questions using pattern matching and substitution
methodology. At that time, many variations of the chatbot were made and
one of the most well-known is called DOCTOR, this was made to respond
like a Rogerian psychotherapist. In this instance, the therapist
"reflects" on questions by turning the questions back at the patient.
While ELIZA was a humble beginning, this surely paved the way for
further research in the field of Chatbot and natural language processing
in the years to come. For a try out ELIZA, please use the following
link: [ELIZA](https://web.njit.edu/~ronkowit/eliza.html).

#### Rise of Recurrent Neural Networks (1980s)

Moving on into the late 20th century, we saw the emergence of neural
networks, which were deeply inspired by the human brain and its
interconnected neurons. Among these, Recurrent Neural Networks (RNN)
were the first to come in 1986 and they gained instant popularity from
the world. Unlike traditional feedforward neural networks, where the
flow of information was in one direction, RNNs could remember previous
inputs in their internal state or memory and answer questions based on
context. They are trained to process and convert a sequential data input
into a specific sequential data output and have a feedback loop, making
them suitable for natural language processing (NLP) tasks. While RNNs
were a significant step forward, they had limitations, especially with
long sentences. In simple words, they are not good at retaining memory
and suffer from long term memory loss. In technical terms, RNNs had a
problem of vanishing gradient. For a general description for RNNs, you
can visit the following
link: [RNN](https://medium.com/@researchgraph/an-introduction-to-recurrent-neural-networks-rnns-802fcfee3098).

#### Rise of Long Short Term Memory (1990s)

Long Short Term Memory (LSTM) came up in 1997. LSTM was a specialised
type of RNN. Their primary advantage was their ability to remember
information over long sequences. Thus, it overcame the short term memory
limitations of RNNs. LSTM has a unique architecture: they have an input
gate, a forget gate, and an output gate. These gates determined how much
information should be memorised, discarded, or output at each step. This
selective ability to memorise or forget helped LSTMs maintain relevant
information in their memory, making them more efficient at capturing
long-term dependencies from sentences. For example, it is able to
resolve the coreference resolution compared to RNNs.

#### Gated Recurrent Network (2010s)

In 2014, Gated Recurrent Units (GRU) came. They were designed to solve
some of the same problems as LSTMs but with a simple and more
streamlined structure. Just like LSTMs, GRUs were designed to combat the
vanishing gradient problem, allowing them retain long-term dependencies
in sentences. GRUs simplified the gating by using only two gates: an
update gate which determined how much of the previous information to
keep versus how much of the new information to consider ; and a reset
gate which determined how much of the previous information to forget.
The reduced gating in GRUs made them more efficient in terms of
computation.

#### Rise of Attention Mechanism (2014)

As it turns out RNNs, as well as RNN based variants LSTM and GRU, were
not great at retaining the context when it was far away. The NLP world
and their problems needed something more and that gave birth to the
concept of attention. The introduction of the attention mechanism marked
a significant paradigm shift in sequence modelling, offering a fresh
perspective compared to previous architectures. RNN would process
sentences with a fixed-size context vector that try to cram all the
information of a source sentence regardless of its length, into a single
fixed-length vector and because of this, their performance deteriorates
as the sentence length increases. In contrast, attention allows the
model to look back to the entire source sequence dynamically, selecting
different parts based on their relevance at each step of the output.
This ensures that no crucial information is lost or diluted, especially
in longer sequences.

<figure>
<img
src="https://cdn-images-1.medium.com/max/1000/0*0AsaDcZVgAlp2Tac" />
<figcaption>Performance Comparison. Source: <a
href="https://www.youtube.com/watch?v=K7o5_Fj7_SY">Brief History of
Large Language Models &amp; Generative AI | Evolution of NLP from Eliza
to ChatGPT</a></figcaption>
</figure>

The figure above illustrates the decline in RNN performance compared to
Attention Model as the length of the input sentence increases.

#### The invention of Transformers Architecture (2017)

Transformers came out in 2017 with the paper "*Attention is all you
need*" from Vaswani and colleagues of a Google Team. This new type of
architecture relied on an attention mechanism to process sequence. As
its core, it is composed of an encoder and decoder, each with multiple
stacked layers of self-attention and feed-forward neural networks. A
standout feature is the "multi-head" attention, allowing it to focus on
different parts of the input sentence simultaneously, capturing various
contextual nuances. Another strength was its ability to process
sequences in parallel rather than sequentially. These advantages enable
transformers to lay the foundation for subsequent models like BERT, GPT
and more, driving us into a new era of LLMs.

#### Emergence of Large Language Models (2018-onwards)

With the success of transformers, the next logical step was scaling.
This kick started with Google's BERT model which was released in the
year 2018. Unlike previous models that processed text either
left-to-right or right-to-left, BERT was designed to consider both
directions simultaneously, hence the name: Bidirectional Encoder
Representations from Transformer (BERT). Pre-trained on vast amounts of
text, BERT was the first proper foundational language model that could
be fine-tuned for specific tasks, setting new performance standards
across various benchmarks. With Open AI releasing its GPT-2 Model and
Google releasing its T5 Model in 2019, thereafter GPT-3 came up in 2020,
etc. These LLMs could perform innumerable tasks, marking a paradigm
shift in AI capabilities.

<figure>
<img
src="https://cdn-images-1.medium.com/max/1000/0*BRJhuhXLfuzhG3bb" />
<figcaption>Timeline of recent years large language models. Source: <a
href="https://www.nextbigfuture.com/2023/04/timeline-of-open-and-proprietary-large-language-models.html">https://www.nextbigfuture.com/2023/04/timeline-of-open-and-proprietary-large-language-models.html</a></figcaption>
</figure>

### Conclusion

The evolution of language models from simple rule-based systems to
complex intelligence models shows significant advances in AI technology.
Today, large language models (LLMs) are more than just tools for
enhancing text-based applications, they are increasingly capable of
understanding and communicating with humans.

Additionally, these language models are also able to handle not just
text but also images and sounds, known as multimodal LLMs. Capable of
processing and generating multiple mode data, these models integrate
text, images, audio and video to comprehensively understand and analyse
different forms of data. Multimodal LLMs have a range of applications
including extracting text from digital images, understanding complex
signs, deciphering ancient handwriting, analysing speech files for
summarisation, transcription.

Through simplifying complex text, multimodal LLMs transform the way we
interact with technology and could make it more accessible and
responsive to human needs. In short, these LLMs are becoming powerful
partners for humans, helping us tackle multiple tasks and simplifying
our lives in multiple ways.

### References

- Brief History of Large Language Models & Generative AI \| Evolution of
 NLP from Eliza to ChatGPT (no date)
 [www.youtube.com.](http://www.youtube.com.) Available at:
 <https://www.youtube.com/watch?v=K7o5_Fj7_SY> .
- Ibrahim, M. (2023) An Overview of Large Language Models (LLMs), W&B.
 Available
 at:<https://wandb.ai/mostafaibrahim17/ml-articles/reports/An-Overview-of-Large-Language-Models-LLMs---VmlldzozODA3MzQz?galleryTag=llm#what-is-a-large-language-model>?.
- Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L.,
 Gomez, A. N., Kaiser, L., & Polosukhin, I. (2017). Attention Is All
 You Need (Version 7). arXiv.
 <https://doi.org/10.48550/ARXIV.1706.03762>

‌

![](https://medium.com/_/stat?event=post.clientViewed&referrerSource=full_rss&postId=3c2efa517112){width="1"
height="1"}

#### Attention mechanism not getting enough attention

<figure>
<img
src="https://cdn-images-1.medium.com/max/1000/0*rNlIXsxZSIs6p63O" />
<figcaption>Image from Unsplash.</figcaption>
</figure>

### Author

- Dhruv Gupta (**ORCID**:
 [0009--0004--7109--5403](https://orcid.org/0009-0004-7109-5403))

### Introduction

As discussed in this
[article](https://medium.com/@researchgraph/rnns-vs-grus-vs-lstms-d69fd3b3f455),
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. Transformer-based models which first came out in 2017 took the
Natural Language Processing (NLP) world by storm. They were initially
introduced to solve the problem of sequence-to-sequence translation.
However, they have now become the backbone of almost all the generative
AI models. Models like GPT-3 and BERT, use large transformer-based
models for training on huge amounts of data. In this article, we will
discuss the architecture of transformer-based models and how the
attention mechanism works.

### Transformers

Transformer-based models introduced two new concepts which changed the
field of NLP forever. They were attention-based models and
encoder-decoder models. The concept of attention-based models allowed
the language models to focus on only important parts of the text. This
gave the model the ability to comprehend the long-term dependencies. In
addition to this, the attention mechanism along with the encoder-decoder
architecture of the transformer, empowered the model to comprehend the
subtle nuances in the text and get the textual data, like how we humans
do it.

### What is the Attention mechanism?

The attention mechanism allows a machine learning model to emphasise
certain aspects of the input data and this forms the heart of a
transformer model. However, the transformer model uses a set of
self-attention blocks. Self-attention blocks allow the model to focus on
different positions of a single sequence thereby computing a
representation of the sequence. So the important question is how does
this attention mechanism work?

#### **Keys, Values, and Queries?**

Keys, values, and queries are the heart of the self-attention mechanism.
They are used to calculate the self-attention weights using the input
**X**. The input **X** is multiplied by weight matrices that are learnt
during training.

- Query Vector **(Q) = X Wq**. It can be thought of as the current word.
- Key Vector **(K) = X Wk**. This acts as the index value of the
 value vector.
- Value Vector **(V) = X Wv**. Can be considered as the information
 stored in the input word.

What self-attention does is that for every query (**Q**) the most
similar key **K**, is calculated using the dot product between **Q** and
**K**. The dot product is then multiplied with a v vector to get the
information stored in the input word.

<figure>
<img src="https://cdn-images-1.medium.com/max/628/0*viA1Ma9jV8Jen1IW" />
<figcaption>Attention equation being used in transformers. Source:
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez,
A.N., Kaiser, L. and Polosukhin, I. (2017). Attention Is All You
Need. Link:<a
href="https://doi.org/10.48550/arXiv.1706.03762">https://doi.org/10.48550/arXiv.1706.03762</a></figcaption>
</figure>

### Architecture

The transformer uses an encoder-decoder architecture. The idea behind
such an architecture is that the encoder processes the input data and
transforms it into a different representation, which is then
subsequently decoded by the decoder to produce the desired output.
Transformers which were originally designed for translation use this
architecture a bit differently. The encoder is given the input sentence
and the decoder is given the same sentence in the target language.
However, the decoder only gets the words that have been translated. For
example: if there are 5 words in a sentence and three have been
translated. The decoder will get the already translated words and the
original input sentence and will try to predict the fourth word.

<figure>
<img src="https://cdn-images-1.medium.com/max/693/0*HyLiYiY_6k4XOy09" />
<figcaption>Encoder Decoder cell in transformer</figcaption>
</figure>

#### **Diving into the Architecture**

As discussed in the above section the transformer model consists of an
encoder-decoder model.

<figure>
<img
src="https://cdn-images-1.medium.com/max/1000/0*KuZhQSjOQVkOx27s" />
<figcaption>Complete Architecture of transformer block. Source: Vaswani,
A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N.,
Kaiser, L. and Polosukhin, I. (2017). Attention Is All You
Need. Link:<a
href="https://doi.org/10.48550/arXiv.1706.03762">https://doi.org/10.48550/arXiv.1706.03762</a></figcaption>
</figure>

#### Encoder

The input data is first converted into word embeddings which are then
concatenated with the positional embeddings. The positional embeddings
play an important role because the word embeddings themselves lack the
positional information which plays a crucial role in textual data. The
encoder model has two sub-layers: multi-head self-attention and the
feed-forward neural network. The multi-head self-attention model
consists of a self-attention mechanism which is applied parallelly. This
output is then passed through to the feed-forward neural network which
then learns from the output of the attention model.

#### Decoder

The decoder also consists of a similar sub-layer as the encoder. There
is a masked multi-head attention block which works on the output vectors
of the previous iteration. The multi-head attention block works on the
output of the encoder and masked multi-head attention in the decoder.
The output of the attention block is then passed through to a fully
connected feed-forward neural network which then produces the output
probability for the next words.

The output of the decoder model is fed into the decoder again just like
a typical RNN model. The masked multi-head attention block is used to
mask the future words so that the decoder only generates the output
using the previously seen outputs and previously seen input from the
encoder model.

### Are they still worthy?

Transformer models which first came out in 2017 for language translation
have changed the face of NLP. While NLP tasks are where transformers are
at their peak, they have a lot of other applications outside of text
processing. They can be used for tasks such as speech recognition, image
captioning, text classification, among others.

Apart from solving NLP-related tasks, transformer models have also
formed the backbone of the new-age generative AI models. Most of the
generative AI models such as GPT, GPT-3, and BERT have stacks of
transformer models in them because of their excellent feature extraction
capability. Additionally, the newer version of generative AI models such
as RAG also has transformer-based encoder-decoder models working in
the backend.

Additionally, big companies such as Google, Facebook, Vault, and
Grammarly which are heavily focused on NLP-based applications still use
transformer-based models in the backend. Their ability to accurately
recognise patterns and context in data makes them an invaluable tool in
the field of NLP.

Therefore, in conclusion, with continued advancements in the field of
LLM, transformers have a big role to play. Hence, with a vast array of
applications and even more to follow, transformers are still worthy of
solving almost all complex NLP-based tasks.

<figure>
<img
src="https://cdn-images-1.medium.com/max/1000/0*lAJNOh-EtkhBAAeQ" />
<figcaption>Source: <a
href="https://en.meming.world/wiki/I%27m_Still_Worthy">https://en.meming.world/wiki/I%27m_Still_Worthy</a></figcaption>
</figure>

### **References**

- Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez,
 A.N., Kaiser, L. and Polosukhin, I. (2017). *Attention Is All You
 Need*. \[online\] arXiv.org.
 doi:https://doi.org/10.48550/arXiv.1706.03762.
- Kulshrestha, R. (2020). *Transformers*. Medium. Available at:
 <https://towardsdatascience.com/transformers-89034557de14.>
- huggingface.co *How do Transformers work? --- Hugging Face NLP
 Course*. \[online\] Available at:
 <https://huggingface.co/learn/nlp-course/en/chapter1/4>
- Kulshrestha, R. (2020b). *Understanding Attention In Deep Learning*.
 Medium. Available at:
 <https://towardsdatascience.com/attaining-attention-in-deep-learning-a712f93bdb1e.>

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