Research Graph

Let’s explore GR-2 together.Continue reading on Medium »

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

Zijian Yang

How to Use FLUX 1.1 Online for Free: A Tutorial on Generating Ultra Realistic Images and Editing Tools


 A Step-by-Step Guide to Using FLUX 1.1, FLUX Fill, Canny, Redux Tools, and Camera Filename Prompt Techniques for Creating or Editing Images in Any Style and Realism


 Author

Zijian Yang (

 ORCID:

0009–0006–8301–7634)

 Introduction to FLUX 1.1

On

How to Use FLUX 1.1

Discover Le Chat: Free ChatGPT Alternatives with Canvas, Internet Search, and Flux.1 Image GenerationContinue reading on Medium »

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

Hands-On Guide to Experiencing Claude’s Computer-Use Feature Without Any Coding BackgroundContinue reading on Medium »

How to Use Claude to Control Your Computer: A Step-by-Step Computer Use Tutorial

How AI is Transforming Medicine and Improving Lives AI in the Healthcare DomainContinue reading on Medium »

The Future of Healthcare

Anthropic’s Groundbreaking AI, Claude 3.5, Uses Computers Like a Human and Becomes a Game-Changer in AutomationContinue reading on Medium »

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

Revolutionising On-Device AI: High-Performance, Low-Resource Language Models for Edge ComputingContinue reading on Medium »

Ministral — The World’s Best Edge Language Model

How Apple’s Research Exposes AI’s Inability to Reason and OpenAI Highlights ChatGPT’s Name-Based BiasesContinue reading on Medium »

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

OpenAI Codex CLI

Stability AI’s latest release offering New Models and FeaturesContinue reading on Medium »

Stable Diffusion 3.5

Working ExamplesContinue reading on Medium »

What is Glif?

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

Mingrui Gao

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?

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…

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?

Australian National University

Dhruv Gupta

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

Amanda Kau

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?

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

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

Vaibhav Khobragade

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

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

Xuzeng He

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

Aishwarya Nambissan

Swinburne University of Technology

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

Aditya Iyengar

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

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. The process often involves wrestling with complex dependencies, managing environment configurations, and navigating the intricacies of containerisation.

What is Cog?

Wendi Fan

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

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

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

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

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

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

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

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

Qingqin Fang

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

Wenyi Pi

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)

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

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 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

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

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

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

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?

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

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

Amir Aryani

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

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

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

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 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

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

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)

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

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

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

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

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

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

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

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”?

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

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

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

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

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

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

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

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?

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

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)

Aland Astudillo

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?

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

Evaluating Cohere’s Aya Vision Against GPT-4V Across Key TasksContinue reading on Medium »

Exploring Aya Vision: Cohere’s Multimodal Model and Its Comparison with GPT

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

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

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

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?

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

Multimodal Chain-of-Thought Reasoning in Language Models

A Growing Threat to Democracy and TrustContinue reading on Medium »

AI and Misinformation

How to Enhance Disaster Early Warnings with Tech ToolsContinue reading on Medium »

AI to the Rescue

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

Bridging the global mental-health gap with chatbots, data-driven insights and ethically aligned careContinue reading on Medium »

How AI Transforms Mental Wellness: From Applications to Essential Questions

Reasoning, Coding, and Conversation: Evaluating Claude’s Evolution as a Thinking PartnerContinue reading on Medium »

Beyond the Benchmark: A Hands-On Evaluation of Claude 3.7 Sonnet

A Scalable, Evidence-Based ApproachContinue reading on Medium »

From Manual Analysis to Automated Insight: Building a Healthcare AI Taxonomy Pipeline

A practical evaluation of Stack AI’s no-code platform through the lens of building an intelligent job recommender systemContinue reading on Medium »

Stack AI Platform Review: Experience and Insights from an Implementation Perspective

A no-code guide to building a conversational AI assistant with speech capabilities using n8n, OpenAI, and ElevenLabsContinue reading on Medium »

From Text to Talk: Building a Voice-Enabled AI Chatbot with n8n

How Persistent Identifiers Are Creating a Navigable Map of KnowledgeContinue reading on Medium »

Stories by Research Graph on Medium

From Manual Analysis to Automated Insight: Building a Healthcare AI Taxonomy Pipeline

Stack AI Platform Review: Experience and Insights from an Implementation Perspective

Beyond the Benchmark: A Hands-On Evaluation of Claude 3.7 Sonnet

From Text to Talk: Building a Voice-Enabled AI Chatbot with n8n

How AI Transforms Mental Wellness: From Applications to Essential Questions

Exploring Aya Vision: Cohere’s Multimodal Model and Its Comparison with GPT

Google Gemma 3: Hands-on Testing and Comparative Analysis

How to use the new Research Graph API?

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