Australian National University

Qingqin Fang

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

Wendi Fan

Let’s explore GR-2 together. Author Wendi Fan (

 ORCID: 0000–0003–0284–9166

) Introduction GR-2 is a cutting-edge generative model designed for versatile and generalisable robot manipulation, developed by the Robotics Research Team at ByteDance Research. It represents a significant step forward in robotics, enabling robots to execute a wide range of manipulation tasks in various environments.

GR-2: A Generative Video-Language-Action Model for Robot Manipulation

Vaibhav Khobragade

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

Wenyi Pi

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

Amanda Kau

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

Dhruv Gupta

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

Zijian Yang

Le Chat by Mistral: Access ChatGPT-Like Features, Image Generation, Canvas, Artifact, and More for Free


 Discover Le Chat: Free ChatGPT Alternatives with Canvas, Internet Search, and Flux.1 Image Generation


 Author

Zijian Yang (

 ORCID:

0009–0006–8301–7634)

 Introduction

Imagine accessing premium ChatGPT features — Canvas, internet search, and PDF

Le Chat by Mistral: Access ChatGPT-Like Features, Image Generation, Canvas, Artifact, and More for…

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

Xuzeng He

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

Swinburne University of Technology

Nakul Nambiar

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

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

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

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

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

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?

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

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

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

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

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

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”?

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)

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

Science in the age of large language models

Generative AI entails a credit–blame asymmetry

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

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?

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

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

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

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

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

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

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

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

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

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

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

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

Aditya Iyengar

How AI is Transforming Medicine and Improving Lives
 
 AI in the Healthcare Domain
 


 Author

Aditya Iyengar (ORCID: 0009–0005–1959–9724)

 Introduction

The rapid integration of Artificial Intelligence (AI) into healthcare is no longer just a futuristic vision — it is happening now and at an extraordinary pace.

The Future of Healthcare

The Most Recent Cutting-Edge Models


 Author

Aditya Iyengar (ORCID: 0009–0005–1959–9724)

 Introduction

The past 6 months marked significant technological leaps in the field of artificial intelligence (AI), especially in the realm of image generation. In this six-month window, multiple innovations redefined the capabilities of text-to-image, video generation, and multimodal AI systems.

The New Era of AI Image Generation

Mingrui Gao

Working Examples


 Author

Mingrui Gao (

 ORCID

: 0009–0005–7271–2677)

 Introduction

Machine learning has revolutionised countless industries, but deploying ML models remains a significant challenge for many data scientists and developers.

What is Cog?

Let’s explore MLflow together. Author Wendi Fan (

 ORCID: 0000–0003–0284–9166

) Introduction MLflow is an open-source platform designed to manage the entire lifecycle of machine learning projects. It helps developers and data scientists streamline their workflows by tracking experiments, managing models, and deploying them efficiently.

MLflow Beginner’s Guide: How to Get Started with MLflow

Tarun Krishnan

Unleashing FLUX1.1 with the BFL API


 Author

Tarun Krishnan (

 ORCID

: 0009–0006–6647–127X)

 Introduction

With the growing demand for

 image generation models

in various applications, the need for

 APIs

that seamlessly integrate these models into developers’ workflows has skyrocketed.

The Fast Lane of Image Generation

Tohfa Siddika

The Godfather of AI Author Tohfa Siddika Barbhuiya (

 ORCID

: 0009–0007–2976–4601) In a monumental moment for the world of Artificial Intelligence (AI), Geoffrey Hinton, known as the “Godfather of AI,” has been awarded the Nobel Prize for his pioneering work in neural networks and deep learning. He has been awarded the 2024 Nobel Prize in physics by the Royal Swedish Academy of Sciences.

Geoffrey Hinton Wins Nobel Prize

Evaluating the Role of Self-Reflection in AI: Innovation or Overcomplication?

Author Tohfa Siddika Barbhuiya (

 ORCID

: 0009–0007–2976–4601) Introduction Artificial Intelligence (AI) has made remarkable strides over the past decade, with new advancements pushing the boundaries of what machines can achieve. One of the latest developments in the field is the introduction of “reflection” within AI models.

The Idea of Reflection Models

OpenAI Canvas and DevDay: Advancing AI Collaboration with Voice Realtime API, Model Distillation, and More Explore OpenAI’s latest tools and features — Canvas, Realtime API, Prompt Cache, Visual Fine-Tuning Author Zijian Yang (

 ORCID:

0009–0006–8301–7634) Introduction OpenAI recently introduced a new feature called Canvas, which is designed to provide ChatGPT users with a more powerful writing and coding collaboration

OpenAI Canvas and DevDay: Advancing AI Collaboration with Voice Realtime API, Model Distillation…

Working Examples Author Mingrui Gao (

 ORCID

: 0009–0005–7271–2677) Introduction In the rapidly evolving landscape of artificial intelligence, Large Language Models (LLMs) have emerged as powerful tools capable of understanding and generating human-like text. While cloud-based services offer convenient access to these models, there’s a growing demand for local, hands-on experimentation.

What is LM Studio?

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

Let’s explore ChatGPT’s Advanced Voice Mode Author Wendi Fan (

 ORCID: 0000–0003–0284–9166

) Introduction In today’s fast-paced world, where convenience and efficiency are paramount, OpenAI’s latest addition to ChatGPT, the

 Advanced Voice Mode

, sets a new standard for human-computer interaction.

Unlocking the Power of ChatGPT’s Advanced Voice Mode

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

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)

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

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

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

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?

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?

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

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?

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?

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

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

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

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

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

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

Ministral — The World’s Best Edge Language Model
 


 Revolutionising On-Device AI: High-Performance, Low-Resource Language Models for Edge Computing


 Author

Tarun Krishnan (

 ORCID

: 0009–0006–6647–127X)

 Introduction

In recent years, there’s been a notable shift in the AI landscape, with many companies moving away from developing massive,

Ministral — The World’s Best Edge Language Model

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

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

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

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

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

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)

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

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

Anthropic’s Groundbreaking AI, Claude 3.5, Uses Computers Like a Human and Becomes a Game-Changer in Automation


 Author

Zijian Yang (

 ORCID:

0009–0006–8301–7634)

 Introduction

Claude 3.5 Receives a Major Upgrade Overnight! As anticipated, Anthropic AI made a significant move this week with the launch of Claude 3.5 Haiku.

New Claude 3.5 Can Control Computer: Outsmarts o1 in Coding and Redefines Agent Capabilities

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

How Apple’s Research Exposes AI’s Inability to Reason and OpenAI Highlights ChatGPT’s Name-Based Biases


 Author

Zijian Yang (

 ORCID:

0009–0006–8301–7634)

 Introduction

As artificial intelligence continues to evolve, the capabilities and limitations of large language models (LLMs) are under increasing scrutiny.

ChatGPT’s Name Bias and Apple’s Findings on AI’s Lack of Reasoning: Major Flaws Revealed

Working Examples


 Author

Mingrui Gao (

 ORCID

: 0009–0005–7271–2677)

 Introduction

In the fast-paced world of artificial intelligence, a new platform is gaining attention by making AI-powered content creation more accessible.

What is Glif?

Stability AI’s latest release offering New Models and Features


 Author

Tohfa Siddika Barbhuiya (

 ORCID

: 0009–0007–2976–4601)

 Stable Diffusion 3.5

by

 Stability AI

marks a major step forward in AI-driven image generation.

Stable Diffusion 3.5

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. Let’s delve into the details of this new AI model by Google.

What is Gemma2?

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…

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…

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

Stories by Research Graph on Medium

#### Let's explore MLflow together. {#lets-explore-mlflow-together.}

<figure>
<img
src="https://cdn-images-1.medium.com/max/371/0*7hbpTxs-CGhYWztO.png" />
<figcaption>MLFlow logo</figcaption>
</figure>

### Author

- [Wendi Fan](https://www.linkedin.com/in/wendi-fan-265996310/)
 (**ORCID: 0000--0003--0284--9166**)

### Introduction

MLflow is an open-source platform designed to manage the entire
lifecycle of machine learning projects. It helps developers and data
scientists streamline their workflows by tracking experiments, managing
models, and deploying them efficiently. MLflow's core components
include:

1. **MLflow Tracking**: This component enables the logging and querying
 of machine learning experiments' parameters, metrics, and models. It
 allows users to track hyperparameter configurations, and training
 outcomes, and compare the performance of different experiments.
2. **MLflow Projects**: This component provides a standardised format
 for defining machine learning projects, and facilitating team
 collaboration. Through MLproject files, MLflow Projects define
 dependencies and entry points, making it easier to reproduce and
 share projects.
3. **MLflow Models**: This component focuses on managing and deploying
 machine learning models. MLflow supports multiple model formats,
 allowing trained models to be saved and later loaded in different
 environments.
4. **MLflow Registry**: This component provides model registration and
 version control, helping teams manage the entire lifecycle of a
 model, from development and testing to deployment and updates.

MLflow is compatible with various popular machine learning libraries,
such as TensorFlow, PyTorch, and scikit-learn, and integrates seamlessly
with containerisation tools like Docker and Kubernetes, enabling rapid
model deployment.

### Setting Up MLflow

Before exploring the rich features MLflow offers, it's essential to set
up the foundational components: the MLflow Tracking Server and the
MLflow UI. This guide will walk you through the steps to get both up and
running smoothly.

### Step 1: Install MLflow from PyPI

MLflow is readily available on PyPI, and installing it is as simple as
running the following pip command:

 pip install mlflow

### Step 2: Launch the MLflow Tracking Server

Next, let's test if the MLflow UI is working properly. To start, you'll
need to launch the MLflow Tracking Server. Make sure to keep the command
prompt open during the tutorial, as closing it will stop the server.
Here's the command you need to run in your terminal:

 mlflow server --host 127.0.0.1 --port 8080

After running the above command, you will see the URL for the running
MLflow UI displayed in the terminal, as shown in the image below.

<figure>
<img
src="https://cdn-images-1.medium.com/max/1024/0*Ulas6Qq9bFIZnjkT.jpg" />
<figcaption>MLFlow server is running in the terminal</figcaption>
</figure>

By opening this URL in your browser, you will see the UI as depicted in
the following image:

<figure>
<img
src="https://cdn-images-1.medium.com/max/1024/0*KI_NTlQyagXVO4ZU.jpg" />
<figcaption>MLFlow UI</figcaption>
</figure>

### MLflow Tutorial

In just a few minutes, this quickstart will guide you through key MLflow
concepts, including how to log parameters, metrics, and models, the
basics of using the MLflow fluent API, how to register a model during
the logging process, how to navigate to the registered model in the
MLflow UI, and how to load a logged model for inference.

Here, we use a simple example to train a model. This code primarily
focuses on training and testing a **Logistic Regression** model using
the **Iris dataset**, and calculating the accuracy of its predictions.
The code loads the classic **Iris dataset**, a public dataset containing
150 flower samples, each with 4 features (sepal and petal lengths and
widths), and divided into 3 categories: Setosa, Versicolor, and
Virginica. The model predicts the flower category based on the 4
features (sepal length, sepal width, petal length, and petal width).
Additionally, the code splits the dataset into training and test sets,
with 80% of the data used for training (X_train and y_train) and 20% for
testing (X_test and y_test).

### Step 1: Set the Tracking Server URI

If you're using a managed MLflow Tracking Server or if you're running a
local tracking server, ensure that you set the tracking server's
uri using:

 import mlflow
 mlflow.set_tracking_uri(uri="http://<host>:<port>")

### Step 2: Train a model and prepare metadata for logging

In this section, we will log a model using MLflow. The steps include
loading and preparing the Iris dataset, training a Logistic Regression
model, evaluating its performance, and then preparing the model's
hyperparameters and metrics for logging.

 import mlflow
 from mlflow.models import infer_signature
 import pandas as pd
 from sklearn import datasets
 from sklearn.model_selection import train_test_split
 from sklearn.linear_model import LogisticRegression
 from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score

 # Load the Iris dataset
 X, y = datasets.load_iris(return_X_y=True)
 # Split the data into training and test sets
 X_train, X_test, y_train, y_test = train_test_split(
 X, y, test_size=0.2, random_state=42
 )
 # Define the model hyperparameters
 params = {
 "solver": "lbfgs",
 "max_iter": 1000,
 "multi_class": "auto",
 "random_state": 8888,
 }
 # Train the model
 lr = LogisticRegression(**params)
 lr.fit(X_train, y_train)
 # Predict on the test set
 y_pred = lr.predict(X_test)
 # Calculate metrics
 accuracy = accuracy_score(y_test, y_pred)

### Step 3: Log the model and its metadata to MLflow

In the next step, we will log the trained model, the hyperparameters
used for fitting the model, and the loss metrics calculated from
evaluating its performance on the test data to MLflow. The process
involves the following steps:

1. Initiate an MLflow run context to start a new run where we will log
 the model and related metadata.
2. Log the model's parameters and performance metrics.
3. Tag the run for easy retrieval in the future.
4. Register the model in the MLflow Model Registry while saving
 the model.



 # Set our tracking server uri for logging
 mlflow.set_tracking_uri(uri="http://127.0.0.1:8080")

 # Create a new MLflow Experiment
 mlflow.set_experiment("MLflow Quickstart")
 # Start an MLflow run
 with mlflow.start_run():
 # Log the hyperparameters
 mlflow.log_params(params)
 # Log the loss metric
 mlflow.log_metric("accuracy", accuracy)
 # Set a tag that we can use to remind ourselves what this run was for
 mlflow.set_tag("Training Info", "Basic LR model for iris data")
 # Infer the model signature
 signature = infer_signature(X_train, lr.predict(X_train))
 # Log the model
 model_info = mlflow.sklearn.log_model(
 sk_model=lr,
 artifact_path="iris_model",
 signature=signature,
 input_example=X_train,
 registered_model_name="tracking-quickstart",
 )

The purpose of this code is to log the machine learning model's training
process, including the hyperparameters, performance metrics, and the
model itself, and save this information using MLflow. It also registers
the model in the MLflow Model Registry, allowing the model to be reused
in different environments. This greatly facilitates the tracking of
experimental results and the replication and deployment of the model in
the future.

To view the results of our run, we can navigate to the MLflow UI. Since
the Tracking Server is already running at
[http://localhost:8080](http://localhost:8080/), simply enter that URL
in your browser.

Once the site opens, you'll see a screen similar to the one below:

<figure>
<img
src="https://cdn-images-1.medium.com/max/1024/0*pA_xgtcZ_tUPdZeo.png" />
<figcaption>The main MLflow Tracking page, showing Experiments that have
been created, downloaded from MLflow website</figcaption>
</figure>

By clicking on the name of the experiment we created ("MLflow
Quickstart"), you'll be presented with a list of all the runs associated
with that experiment. In the table view on the right, you'll see a
randomly generated name for the run.

Clicking on the run's name will take you to the run details page, where
you can view all the information we've logged. The key elements are
highlighted below to show how and where this data is displayed within
the UI:

<figure>
<img
src="https://cdn-images-1.medium.com/max/1003/0*X4VHgXRwolBgAPMR.png" />
<figcaption>The run view page for our run, downloaded from
MLflow website</figcaption>
</figure>

Our model has also been saved in the Registered Model section named
"tracking-quickstart":

<figure>
<img
src="https://cdn-images-1.medium.com/max/1024/0*0sbVjnycI_e2j_R6.jpg" />
<figcaption>Registered model page</figcaption>
</figure>

### Step 4: Load the model as a Python Function (pyfunc) and use it for inference

After logging the model, we can perform inference by:

1. Loading the model using MLflow's pyfunc flavor.
2. Running predictions on new data with the loaded model.



 # Load the model back for predictions as a generic Python Function model
 loaded_model = mlflow.pyfunc.load_model(model_info.model_uri)

 predictions = loaded_model.predict(X_test)
 iris_feature_names = datasets.load_iris().feature_names
 result = pd.DataFrame(X_test, columns=iris_feature_names)
 result["actual_class"] = y_test
 result["predicted_class"] = predictions
 result[:4]

This code loads a previously saved model from MLflow and makes
predictions on the test data. It then combines the actual labels of the
test data with the predicted results into a table, displaying the first
four rows. This allows us to verify the model's performance on the test
data and visually compare the predictions against the actual labels.

The output of this code will look something like this:

<figure>
<img
src="https://cdn-images-1.medium.com/max/872/0*okvNyjGiPwZSuTmE.jpg" />
<figcaption>Outcomes of Prediction by loaded model</figcaption>
</figure>

Of course, we can also specify the pre-trained model we want to run by
selecting the run_id, and we can provide new data for it to make
predictions. Below is a simple code example:

 import mlflow.pyfunc
 import numpy as np

 # Set the MLflow Tracking URI to your server's address
 mlflow.set_tracking_uri("http://127.0.0.1:8080")
 # Load the model using the run ID logged in MLflow
 run_id = "Your_run_id" # Replace with the run ID found in the MLflow UI
 model_uri = f"runs:/{run_id}/iris_model"
 loaded_model = mlflow.pyfunc.load_model(model_uri)
 # Define new test data
 new_data = np.array([
 [5.1, 3.5, 1.4, 0.2],
 [6.7, 3.1, 4.7, 1.5]
 ])
 # Use the loaded model to make predictions
 predictions = loaded_model.predict(new_data)
 # Print the prediction results
 print("Predictions:", predictions)

In short, MLflow makes it convenient to train our models and save them
for future use. When needed, we can easily load a previously trained
model to run predictions, or even share it with others. Additionally,
MLflow allows us to record detailed results from each training session,
enabling effective version control of our models.

### Differences Between Runs and Registered Models

Sometimes, we might wonder about the difference between a run and a
Registered Model, especially since both can be used to load previously
trained models.

- **Run-based models**: Best suited for development and experimentation,
 allowing for quick iteration and debugging.
- **Registered Models**: Ideal for production, offering version control
 and lifecycle management, making it easier to manage and update models
 in a team environment.

#### **Key Differences Between the Two**

<figure>
<img
src="https://cdn-images-1.medium.com/max/1024/1*EDnXpaXr85PQ0L7MjhzeFQ.png" />
</figure>

If you want to use a registered model in a production environment, you
can either register the model during training or register an existing
model from a run as a Registered Model. For example:

 mlflow.register_model(model_uri=f"runs:/{run_id}/iris_model", name="tracking-quickstart")

Here's an example showing how to use registered models as loaded models:

 import mlflow.pyfunc

 # Load the model using the Registered Model name and version number
 model_name = "your_registered_model_name"
 model_version = 1 # Specify the version to load
 model_uri = f"models:/{model_name}/{model_version}"
 loaded_model = mlflow.pyfunc.load_model(model_uri)
 # Use the model to make predictions
 predictions = loaded_model.predict(new_data)

### Conclusion

MLflow is a powerful platform that simplifies the management of machine
learning projects, offering seamless integration for tracking
experiments, saving models, and deploying them across various
environments. By providing easy-to-use tools like the MLflow Tracking
Server, Model Registry, and the flexible pyfunc interface, MLflow
enables developers and data scientists to streamline the entire
lifecycle of their models. Whether you are in the experimental phase or
deploying models to production, MLflow's robust features ensure that
your workflow is organised, scalable, and reproducible. By leveraging
MLflow, teams can efficiently manage models, track results, and
collaborate more effectively, all while ensuring the scalability of
machine learning efforts from development to production.

### References

- *MLflow Tutorial Page --- Getting Started with MLflow* [Read
 me](https://mlflow.org/docs/latest/getting-started/index.html),
 accessed on 18 Oct, 2024. 2) *MLflow Team --- MLflow Tracking
 Quickstart*
 [Read me](https://mlflow.org/docs/latest/getting-started/intro-quickstart/index.html).

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