Australian National University

Wenyi Pi

Neo4j APOC Library Use Case

 Author

Wenyi Pi (

 ORCID:

0009–0002–2884–277)

 Introduction

In the realm of Neo4j, the APOC (Awesome Procedures on Cypher) library stands as a powerful tool. Previously, We have talked about the importance of APOC in optimising Cypher queries and improving query efficiency in our article Exploring Methods of Cypher Query Optimisations.

Introduction to APOC: Enhancing Neo4j Capabilities

Xuzeng He

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

 ORCID:

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

 nodes

(or vertices) and their relations are called

 edges

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

Recent Advances in using Machine Learning with Graphs

Vaibhav Khobragade

Author: Vaibhav Khobragade (

 ORCID:

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

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

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

 ORCID

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

 large language model (LLM)

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

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

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

Recent Advances in using Retrieving Multimodal Information for Augmented Generation

Amanda Kau

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

Author:

 Amanda Kau (ORCID:


 0009–0004–4949–9284


 )

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

Harnessing The Power of Knowledge Graphs in Question Answering

Qingqin Fang

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

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

 Introduction

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

Hallucination in Large Language Models and Two Effective Alleviation Pathways

Yunzhong Zhang

Efficient creation of a stoplight report with data dashboard images

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

How to use GPT-4V For Stoplight Report

Dhruv Gupta

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

RAG: The next big thing after LLMs?

Harvesting the Power and Knowledge of Large Language Models


 Author

Amanda Kau

 (ORCID:

0009–0004–4949–9284

 )

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

Automated Knowledge Graph Construction with Large Language Models

Swinburne University of Technology

Nakul Nambiar

Zhuochen Wu

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

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

Dhruv Gupta

Prompt Engineering — Part 2


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

Author Dhruv Gupta

 (ORCID:

0009–0004–7109–5403

 )

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

Prompt Engineering — Part 2

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


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

Author:

 Wenyi Pi (ORCID:


 0009-0002-2884-2771


 )

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

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

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

 ORCID:

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

Recent Advances in Using Machine Learning with Graphs — Part 2

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

Author: Vaibhav Khobragade (

 ORCID:

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

Ethics and AI: Confronting the Challenges Ahead

How to efficiently retrieve information for different applications

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

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

Amir Aryani

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

Mini RAG using Neo4j

Aishwarya Nambissan

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

Typesense and Neo4j in a hybrid information retrieval solution

Amir Aryani

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

 Background

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

19 Clustering Techniques

Understanding the Evolutionary Journey of LLMs Author Wenyi Pi (

 ORCID

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

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

Understanding Sequential Data Modelling with Keras for Time Series Prediction

 Author

Wenyi Pi (

 ORCID

: 0009–0002–2884–2771) Introduction Recurrent Neural Networks (RNNs) are a special type of neural networks that are suitable for learning representations of sequential data like text in Natural Language Processing (NLP). We will walk through a complete example of using RNNs for time series prediction, covering

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

Incorporating Knowledge Graphs to explain reasoning processes


 Author

Amanda Kau (

 ORCID:

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

Combining Knowledge Graphs With Language Models for Interpretability

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

 ORCID:

0009–0003–5348–4264)

 Introduction to OpenAlex

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

Unveiling Research Trends through OpenAlex Visualization

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

 ORCID

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

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

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


 Author:


 Dhruv Gupta (ORCID:


 0009–0004–7109–5403


 )

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

Language Models: Deep Dive into BERT

Attention mechanism not getting enough attention Author Dhruv Gupta (

 ORCID

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

Transformers Models in NLP

Exploring the potentials and limitations of Vision Language Models

Author:

 Amanda Kau (ORCID:


 0009–0004–4949–9284


 )

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

How Much Can Vision Language Models Really “See”?

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

Author Amanda Kau (

 ORCID

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

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

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

Author · Vaibhav Khobragade (

 ORCID:

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

Three Paradigms of RAG

An introduction to tools supporting PubMed Author: Xuzeng He (

 ORCID:

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

 NCBI

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

Enhancing PubMed: from a Medical Database to beyond

Aland Astudillo

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

How to use GROBID

Using Mistral for Data tagging Author · Xuzeng He (

 ORCID:

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

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

Integrating temporal data into static knowledge graphs

 Author

Amanda Kau

 (ORCID:


 0009–0004–4949–9284


 )

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

Dynamic Knowledge Graphs: A Next Step For Data Representation?

Transformative Advances in Language Models through External Knowledge Integration

Author: Qingqin Fang (

 ORCID:

0009–0003–5348–4264)

 Introduction

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

Deeper understanding of In-Context Retrieval-Augmented Language Models

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

Research Graph 101

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

 ORCID:

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

Large Language Models and Knowledge Graphs: Ways to combine them

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


 Author


 Dhruv Gupta (ORCID:


 0009–0004–7109–5403


 )

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

Prompt Engineering

Boosting Performance for Knowledge Graphs with Neo4j APOC Library

 Author

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

 knowledge graph

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

Exploring Methods of Cypher Query Optimisations

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

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

What is LLMLingua?

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

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

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

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

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

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

Research Collaboration Network

Improving the performance and application of Large Language Models

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

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

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

Author · Vaibhav Khobragade (

 ORCID:

0009–0009–8807–5982)

 Introduction

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

Harnessing Temporal Dynamics: Advanced Reasoning using Temporal Knowledge Graphs

Exploring innovative Strategies in Combating Misinformation with Enhanced Multimodal Understanding Author Wenyi Pi (

 ORCID

: 0009–0002–2884–2771) Introduction Misinformation refers to false or inaccurate information that is often given to someone in a deliberate attempt to make them believe something that is not true. This has a significantly negative impact on public health, political stability and social trust and harmony.

Multimodal Large Language Models for Misinformation Detection and Reasoning

The Three Oldest Pillars of NLP

Author Dhruv Gupta (

 ORCID

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

RNNs vs GRUs vs LSTMs

A Unified and Collaborative Framework for LLM Author · Qingqin Fang (

 ORCID:

0009–0003–5348–4264) Introduction In today’s rapidly evolving field of artificial intelligence, large language models (LLMs) are demonstrating unprecedented potential. Particularly, the Retrieval-Augmented Generation (RAG) architecture has become a hot topic in AI technology due to its unique technical capabilities.

RAG 2.0 is Coming?

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

 ORCID:

0009–0009–8807–5982)

 Introduction

The fusion of Vision-Language Models (

 VLMs

), Generative Models, and Knowledge Graphs (

 KGs

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

Tuning Vision-Language Models and Generative Models with Knowledge Graph

Understanding the Power and Applications of Natural Language Processing

 Author

Dhruv Gupta (

 ORCID:

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

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

Understanding how RNNs work and its applications


 Author

Wenyi Pi (

 ORCID

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

An Introduction to Recurrent Neural Networks (RNNs)

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

 Author

Vaibhav Khobragade (

 ORCID:

0009–0009–8807–5982)

 Introduction

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

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

Addressing Misinformation, Bias, and Privacy Concerns

Author:

 Wenyi Pi (ORCID:


 0009–0002–2884–2771


 )

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

The Ethical and Social Impact of Artificial Intelligence

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

 ORCID:

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

Question Answering enhanced by Knowledge Graph

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


 Author:


 Dhruv Gupta (ORCID:


 0009–0004–7109–5403


 )

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

Knowledge Graphs: Deep Dive into OpenAlex

Techniques to integrate Knowledge Graphs into Language Models

 Author

Amanda Kau

 (ORCID:


 0009–0004–4949–9284


 )

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

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

Automated Knowledge Graph Construction with Large Language Models — Part 2

 Harvesting the Power and Knowledge of Large Language Models

Author Amanda Kau (

 ORCID

:

 0009–0004–4949–9284

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

Automated Knowledge Graph Construction with Large Language Models — Part 2

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

 ORCID:

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

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

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

 ORCID:

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

Working with Graphs: Pre-training and Application

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

 ORCID:

0009–0009–8807–5982)

 Introduction

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

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

Understanding Knowledge Networks From A Graph Perspective


 Author:


 Amanda Kau (ORCID:


 0009–0004–4949–9284


 )

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

Uncovering The Secrets Behind The Most Influential Scholarly Publications With AceMap

Understanding the Balance between Internal Knowledge and External Sources

Author Qingqin Fang (

 ORCID:

0009–0003–5348–4264) Introduction Previous research often emphasized the limitations of LLM’s information acquisition pathways, focusing on enhancing its capabilities in this regard.

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

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

 ORCID:

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

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

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

 ORCID:

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

Fine-tuning Large Language Models: A Brief Introduction

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

 ORCID:

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

Unlocking Intelligence: The Journey from Data to Knowledge Graph

Author Dhruv Gupta (

 ORCID

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

Prompt engineering: A Way to Smartly Use AI

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

 ORCID:

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

Large Language Models for Code Writing: Security Assessment

Research Graph

Google’s Groundbreaking AI Model Explained
 


 Introduction

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

What is Gemma2?

Stories by Research Graph on Medium

**Improving the performance and application of Large Language Models**

<figure>
<img
src="https://cdn-images-1.medium.com/max/1000/0*Jb1q1366lzVqD2JU" />
<figcaption>Image generated with Google’s Gemini, 24
February 2024.</figcaption>
</figure>

### Author

* Amanda Kau (ORCID: [0009-0004-4949-9284](https://orcid.org/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. Many of us
have since learnt --- perhaps when asking ChatGPT a question or
employing it to write our reports --- that LLMs can hallucinate. This
happens when the LLM so eloquently expresses false knowledge that we
might be fooled by it. This major flaw has spurred the popularity of
Retrieval-Augmented Generation (RAG) techniques as a way to optimise an
LLM's responses.

To start off, this article will cover a brief overview of the key
concept behind RAG. Subsequently, a review on several issues behind the
retrieval step of RAG will be presented. In particular, this article
will review ideas on when and what should be retrieved, the quantity of
retrieved documents, effects of data quality, and RAG applied to
different domains. Strategies proposed by the research community will
also be briefly introduced for each challenge.

### **A Brief Introduction To RAG**

<figure>
<img
src="https://cdn-images-1.medium.com/max/1000/0*T6Zr0wCmrTOe83xp" />
<figcaption>Simple diagram about RAG.</figcaption>
</figure>

Using RAG is very much like performing Google searches while writing a
report. When the LLM attempts to generate a sentence without the
requisite knowledge, it needs to reference an external source to gain
that knowledge. Particularly prevalent in tasks like open domain
question answering (ODQA), the LLM may be enquired about a specific
topic which requires in-depth knowledge. Although LLMs are trained with
extensive amounts of diverse data, it is unreasonable to expect the LLM
to have internalised every potential answer and to deliver a response
that an expert might provide.

This is where RAG comes in handy. RAG comprises two components: the
retriever and the generator. The retriever gathers a set of supporting
documents from a given source based on an input query and passes them on
to the generator or LLM. This allows the LLM to avoid hallucinations,
thereby crafting more well-informed and relevant responses.

### **Addressing Retrieval Problems and Strategies**

#### 1. When and What To Retrieve {#when-and-what-to-retrieve}

Many retrieval-augmented language models utilise a single retrieval
process, which is particularly restrictive if the model is tasked to
generate a long passage of text. It is akin to writing a report whilst
only being permitted to reference documents once at the start.
Conversely, models that attempt to do multiple retrievals might do so at
fixed intervals. This seems illogical when we draw parallels to our
report writing scenario. It is like doing an online search every two
sentences we write. When the LLM is routinely bombarded by additional
information it does not require, the LLM might be disoriented, resulting
in it returning incoherent or irrelevant responses.

Some attempts to address this problem have been made, such as the
Forward-Looking Active REtrieval (FLARE) augmented generation method.
The premise of FLARE is simple: retrieve supporting information only
when the LLM signals low confidence indicating a lack of knowledge.

The subsequent challenge revolves around what information to retrieve
for the upcoming sentence. Imagine if you were tasked to find evidence
for a friend who's formulating an argument, but you must provide the
evidence without knowing what their next sentence will be. FLARE targets
this by prompting the LLM to generate a temporary potential sentence and
using this to retrieve documents --- essentially, asking the friend what
they might want to say next before doing your search.

#### **2. Quantity of Retrieved Documents** {#quantity-of-retrieved-documents}

The architectures of some RAG models confine them to retrieving a
certain number of supporting documents. Picture writing a report whilst
only having access to a fixed number of documents --- your viewpoints
and content would be largely based on that limited pool of knowledge. If
that set of documents were replaced, your argument might undergo a
drastic shift as your supporting information might change, resulting in
a lack of consistency in responses. LLMs are vulnerable in a similar way
and are sensitive to the quality of retrieved documents. That is, if
retrieved documents prove irrelevant to the context, the LLM's
output suffers.

One attempt to mitigate this involves clustering the training data as in
the case of MemGM, a memory-augmented generative model. This notion of
memory-augmentation means the model internalises the characteristics of
clusters of responses and uses them to aid response generation. To do
this, the data, which consists of query and response example pairs, is
grouped according to the similarity of queries. Subsequently, when MemGM
retrieves information from this database, the cluster average
representing the cluster's characteristics is returned instead of a set
of documents. This approach dilutes individual responses, hence
decreasing the LLM's sensitivity to individual documents. This also
serves as a means to provide generalised support from a large number of
documents to the LLM.

#### 3. Quality of Retrieved Documents {#quality-of-retrieved-documents}

**Data Recency:** The knowledge encapsulated within LLMs is frozen in
time from when they were trained, preventing them from being able to
provide up-to-the-minute information about the world. In fact, without
taking corrective measures, LLMs become increasingly outdated and
irrelevant over time. Nonetheless, they are still expected to interact
with the ever changing world, so data recency is of critical importance.

One proposed solution is to harvest the power of the Internet. Not only
does it contain the most current information, but years of refinement
have enabled Internet search engines to properly rank results, navigate
online safety and privacy concerns, and more. By allowing RAG models to
access the Internet dynamically, LLM models would always have access to
up-to-date information to craft accurate responses.

**Data Relevance:** Another factor influencing quality is the relevance
of the retrieved results to the context of the conversation or question
posed to the LLM. Ideally, irrelevant retrieved data should not harm the
LLM's performance, but this is not the case. Instead, irrelevant data
tends to mislead the LLM to either provide inaccurate answers or lose
sight of the initial context. This error compounds in multi-hop question
answering scenarios where successive questions cause the LLM to be
misled further with each question.

A simple natural language inference (NLI) model can be employed on top
of the LLM to circumvent this issue. Simply put, the NLI model
identifies the relevance of the retrieved information to the
conversational context. If the retrieved documents are deemed as
irrelevant, the prompt is given directly to the LLM without additional
documents to avoid confusing it. Additionally, if training the LLM is an
available option, studies have shown that even with a relatively small
dataset of 1,000 examples, LLMs can be trained to disregard irrelevant
documents.

#### 4. Problems Generalising to Specific Domains {#problems-generalising-to-specific-domains}

The final challenge discussed in this article occurs when we want the
LLM to have expertise in specific domains, like healthcare or finance.
It was briefly mentioned in the introduction to RAG that it contains a
retriever component. The retriever in the original RAG model was trained
on Wikipedia-based datasets. Therefore, RAG performs well when provided
with domain-specific data that adheres to the Wikipedia article format,
but fails with other data formats. A simple example is financial news,
which is usually succinct in the form of news flashes or tweets, and
does not provide additional context as it is assumed that the reader
possesses the necessary background knowledge. This adds another
disadvantage to the LLM as it will not have the necessary context to
understand the content presented to it.

The RAG model can be further fine-tuned to be domain-specific to target
this specific issue. In fact, a group of researchers proposed
RAG-end2end which extends the original RAG by jointly training all of
its components for a specific domain. However, this is very
computationally expensive and unfeasible for someone who lacks access to
multiple GPUs. Moreover, for broad topics like finance, it is key to
incorporate macroeconomic information and other contextual information
to give the LLM a well-rounded view of the situation, which remains an
area of research.

### Conclusion

In summary, this article has offered insight into some challenges that
may arise when employing RAG and some solutions proposed by the research
community. It highlights the importance of understanding data sources
and retrieval steps performed in RAG such that the retrieval segment may
benefit the LLM instead of harm its performance. In particular, one
should pay attention to when and what documents are retrieved, as well
as the quantity and quality of retrieved documents. Most importantly,
retrieved information should be relevant to the context and desired
domain for the generation of optimal responses.

#### Sources

Jiang, Z., Xu, F. F., Gao, L., Sun, Z., Liu, Q., Dwivedi-Yu, J., Yang,
Y., Callan, J., & Neubig, G. (2023). Active Retrieval Augmented
Generation (Version 2). arXiv.
<https://doi.org/10.48550/ARXIV.2305.06983>

Tian, Z., Bi, W., Li, X., & Zhang, N. L. (2019). Learning to Abstract
for Memory-augmented Conversational Response Generation. In Proceedings
of the 57th Annual Meeting of the Association for Computational
Linguistics. Proceedings of the 57th Annual Meeting of the Association
for Computational Linguistics. Association for Computational
Linguistics. <https://doi.org/10.18653/v1/p19-1371>

Komeili, M., Shuster, K., & Weston, J. (2021). Internet-Augmented
Dialogue Generation (Version 1). arXiv.
<https://doi.org/10.48550/ARXIV.2107.07566>

Yoran, O., Wolfson, T., Ram, O., & Berant, J. (2023). Making
Retrieval-Augmented Language Models Robust to Irrelevant Context
(Version 1). arXiv. <https://doi.org/10.48550/ARXIV.2310.01558>

Siriwardhana, S., Weerasekera, R., Wen, E., Kaluarachchi, T., Rana, R.,
& Nanayakkara, S. (2023). Improving the Domain Adaptation of Retrieval
Augmented Generation (RAG) Models for Open Domain Question Answering. In
Transactions of the Association for Computational Linguistics (Vol. 11,
pp. 1--17). MIT Press. <https://doi.org/10.1162/tacl_a_00530>

Zhang, B., Yang, H., Zhou, T., Ali Babar, M., & Liu, X.-Y. (2023).
Enhancing Financial Sentiment Analysis via Retrieval Augmented Large
Language Models. In 4th ACM International Conference on AI in Finance.
ICAIF '23: 4th ACM International Conference on AI in Finance. ACM.
<https://doi.org/10.1145/3604237.3626866>

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

<figure>
<img
src="https://cdn-images-1.medium.com/max/1000/0*XwvfCyjF7dUWQ7Wj" />
<figcaption>Image generated with Google’s Gemini
12–02–2024.</figcaption>
</figure>

### Author

* Aland Astudillo (ORCID: [0009-0008-8672-3168](https://orcid.org/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. However, there are some challenges such as limited prompt
size, and limited context windows that make LLM not suitable for some.
This is a major issue for tasks that require huge amounts of
information. LLMLingua has been developed as a framework that helps LLMs
in addressing these limitations. In this article, we will review what
LLMLingua is, what it does, how it does it and what we can expect in the
near future.

Picture this scenario, you're tasked with teaching a group of aspiring
chefs how to prepare a complex gourmet meal. You could throw every
recipe detail and culinary term at them, hoping they absorb it all. But
wouldn't it be more effective to break down the instructions into clear,
concise steps, focusing only on the crucial techniques and ingredients?
This, in essence, is the magic of LLMLingua.

Instead of aspiring chefs, imagine training a team of AI apprentices to
tackle complex tasks. Like those aspiring chefs who thrive in learning
to prepare new dishes, LLMs thrive on information. But feeding LLMs with
mountains of raw data can be overwhelming, leading to slow processing
and limited performance. Enter LLMLingua, the innovative tool acting as
the AI chef, meticulously preparing information into bite-sized
instructions that these AI apprentices can easily digest and master.

<figure>
<img
src="https://cdn-images-1.medium.com/max/1000/0*WauCih49veOTPy9V" />
<figcaption>Image generated with Google’s Gemini
12–02–2024.</figcaption>
</figure>

So, how does LLMLingua craft this culinary magic?

It employs two key techniques, similar to how a skilled chef optimises
a recipe:

1. **Ingredient Trimming:** Imagine each piece of information as an
 ingredient in the recipe. LLMLingua, akin to a seasoned chef,
 identifies and discards unnecessary elements like filler words or
 irrelevant details. This "trimming" streamlines instructions,
 keeping only the essential components, just like using only the
 right spices to enhance a dish's flavour.
2. **Recipe Refining**: Even carefully chosen ingredients need precise
 instructions. LLMLingua refines the wording and phrasing of
 instructions, ensuring the AI apprentices grasp the core meaning
 with perfect clarity. It's like the chef rewriting the recipe in
 clear, concise steps, leaving no room for confusion or
 misinterpretation.

Currently, several variations of LLMLingua exist, each tailored to
specific tasks and LLM architectures. One version optimises prompts for
question-answering models, while another focuses on improving
summarization tasks. These variations highlight the adaptability and
evolving nature of this technology.

But LLMLingua isn't alone in the quest for efficient AI. Techniques like
knowledge distillation and parameter reduction also aim to streamline
models. What sets LLMLingua apart is its focus on prompt manipulation,
offering a unique and flexible approach.

<figure>
<img
src="https://cdn-images-1.medium.com/max/1000/0*hAjLbEGPdLOJyziQ" />
<figcaption>Scheme of the LLMLingua framework. Image created by Aland
Astudillo.</figcaption>
</figure>

Imagine teaching a friend a complex recipe. You wouldn't overwhelm them
with every detail; instead, you'd break it down, focusing on key steps
and ingredients. LLMLingua operates similarly, streamlining information
for large language models (LLMs) to achieve peak performance. Here's a
deeper dive into its technical pipeline:

1. **Input Preprocessing**: The journey begins with the "raw"
 information intended for the LLM. LLMLingua analyses it, identifying
 irrelevant elements like filler words or redundant instructions.
 Think of skimming unnecessary details from a recipe book, keeping
 only the crucial steps. This initial "trimming" helps reduce the
 information load on the LLM.
2. **Tokenization**: Next, LLMLingua breaks down the preprocessed text
 into individual units called "tokens," similar to words in a
 sentence. This facilitates further analysis and manipulation.
 Imagine separating the ingredients listed in your recipe into
 individual units --- flour, eggs, milk, etc.
3. **Budget Control**: LLMLingua employs a "budget controller" to
 ensure compression doesn't compromise information integrity. By
 setting a desired compression ratio, it controls how much
 information can be discarded while maintaining optimal performance
 for the LLM. This is like deciding how much of each ingredient to
 use without altering the final dish drastically.
4. **Iterative Compression**: Now comes the magic. LLMLingua utilises
 an iterative algorithm to refine the tokenized information. Each
 iteration analyses the remaining tokens, identifying opportunities
 for further compression while considering their importance for the
 LLM's task. Think of repeatedly reviewing your recipe steps,
 replacing complex techniques with simpler alternatives whenever
 possible.
5. **Instruction Tuning**: Beyond simply removing elements, LLMLingua
 also refines the wording and phrasing of the remaining instructions.
 Imagine rewriting your recipe steps for clarity and accuracy,
 ensuring your friend understands them perfectly. This ensures the
 LLM receives clear and concise instructions, minimising room for
 misinterpretation.
6. **Output Generation**: Finally, the compressed and refined
 information is presented to the LLM as a "streamlined recipe." The
 LLM processes it efficiently, achieving higher performance with
 faster response times and reduced resource consumption. It's like
 your friend effortlessly executing the optimised recipe, producing a
 delicious dish efficiently.

This is just a simplified overview and the actual LLMLingua pipeline
involves complex algorithms and deep learning techniques that can be
reviewed in the research article (See the references).

**Key concepts: Entropy and perplexity**

Additionally, there are two key concepts that allow the control engine
in the LLMLingua framework: entropy and perplexity. These metrics act as
indicators of information content and difficulty, allowing researchers
to gauge the effectiveness of LLMLingua's compression techniques.

**1. Entropy**

Imagine tossing a coin. With one possible outcome (heads or tails), the
entropy is low. Now, consider a hundred-sided die --- with many more
possibilities --- the entropy is higher. Similarly, entropy in language
measures the unpredictability of the next word given the previous ones.
Longer, more complex sentences typically have higher entropy than
shorter, simpler ones.

LLMLingua aims to reduce the entropy of instructions fed to LLMs. By
removing redundant information and focusing on key elements, it
essentially makes the next word more predictable, like simplifying the
die to fewer sides. This lowers the information load on the LLM, leading
to more efficient processing.

**2. Perplexity**

Perplexity builds upon entropy but is presented as an inverse
probability. A lower perplexity value signifies higher predictability,
indicating that the model can more easily anticipate the next word.
Conversely, a high perplexity suggests complex, unpredictable language,
making it harder for the model to process.

In the context of LLMLingua, a decrease in perplexity after compression
reflects improved efficiency. It means the LLM can understand the
compressed instructions with the same accuracy as the original but with
less effort. This translates to faster response times and lower
computational costs.

It's important to note that:

- Both entropy and perplexity are complex measures with nuances beyond
 this simplified explanation.
- LLMLingua's goal isn't to achieve the absolute lowest entropy or
 perplexity, but to find the optimal balance between compression and
 information fidelity.

So, while entropy and perplexity might seem like abstract concepts, they
play a crucial role in understanding how LLMLingua achieves its
efficiency gains.

**Why is LLMLingua the secret ingredient for AI success?**

LLMs are changing the world, translating languages on the fly, composing
personalised poems, and even answering your questions with remarkable
accuracy. But just like our aspiring chefs, their hunger for information
can create limitations. Imagine translating entire novels
word-for-word --- it's slow, resource-intensive, and ultimately
unsatisfying. LLMLingua tackles this head-on, paving the way for faster,
more efficient, and impactful AI applications:

- **Speedy Chatbots**: Ever feel like waiting forever for a chatbot
 response? LLMLingua compresses your questions, enabling chatbots to
 understand you instantly and respond at lightning speed. It's like
 having a personal AI assistant who's always on top of their game,
 eliminating frustrating wait times.
- **Translation on the Go**: Imagine translating entire documents in
 seconds! LLMLingua empowers translation tools to process information
 with laser precision, breaking down language barriers faster than
 ever. Think of it as a universal translator, seamlessly bridging
 communication gaps across cultures and languages like a skilled
 multilingual chef seamlessly navigating different cuisines.
- **Research Revolution**: Picture sifting through mountains of
 scientific data in minutes. LLMLingua empowers AI assistants to
 analyse complex research papers with unmatched efficiency,
 accelerating scientific breakthroughs and discoveries. It's like
 having a tireless research partner who can summarise vast amounts of
 information, highlighting key findings and saving researchers
 countless hours.
- **AI for Everyone**: From personalised learning experiences to
 efficient task management, LLMLingua can revolutionise how AI
 assistants interact with us. Imagine an AI tutor who personalises
 learning modules based on your individual needs or an assistant who
 manages your schedule with laser-focus, all thanks to the power of
 concise and optimised instructions.

But, wait...how do we solve the problem of the limited context size? Let
me introduce you to **LongLLMLingua**!

### What is LongLLMLingua?

Think of LongLLMLingua as the master chef, overseeing the entire
culinary experience. It builds upon LLMLingua's foundation, applying its
compression techniques not just to single instructions, but to entire
sequences of information. This empowers LLMs to process long
contexts --- think research papers, dialogue history, or complex
narratives --- with impressive efficiency and improved performance.

**How does it work?**

LongLLMLingua operates in three key phases:

- **Coarse Compression**: First, it performs a "rough cut," analysing
 the entire context and identifying large sections it can safely
 discard. Imagine skimming extraneous details from a recipe book,
 focusing only on the core steps for each dish.
- **Fine Compression**: Next, it dives deeper, applying LLMLingua's
 techniques to refine remaining information. Think of meticulously
 preparing each dish, ensuring optimal ingredient selection and precise
 instructions.
- **Reordering for Coherence**: Finally, LongLLMLingua ensures the
 compressed information maintains its original meaning and flow.
 Imagine plating each dish in a visually appealing and sequential
 manner, ensuring a cohesive dining experience.

**Why is LongLLMLingua important?**

Long contexts are crucial in various AI applications:

- **Question Answering**: Imagine needing context from several articles
 to answer a complex question. LongLLMLingua helps LLMs retain key
 information across documents, leading to more accurate answers.
- **Machine Translation**: Imagine translating entire books instead of
 single sentences. LongLLMLingua ensures coherence and preserves
 meaning despite lengthy input.
- **Dialogue Systems**: Imagine chatbots understanding intricate
 conversation history. LongLLMLingua enables them to maintain context,
 leading to more natural and engaging interactions.

Real-world examples of LongLLMLingua in action:

- **LongBench Benchmark**: This benchmark measures LLM performance in
 long context scenarios. When applied to GPT-3.5-Turbo, LongLLMLingua
 achieved a 17.1% performance boost with 4x fewer tokens.
- **ZeroScrolls Benchmark**: This benchmark focuses on reading
 comprehension in long contexts. LongLLingua reduced costs by \$27.4
 per 1,000 samples while maintaining performance.

Remember, LongLLMLingua isn't limited to cooking analogies. It's a
powerful tool revolutionising how LLMs handle long contexts,
accelerating progress in various AI domains. By improving efficiency and
maintaining information fidelity, LongLLMLingua opens doors to a future
where AI interactions are more contextual, seamless, and impactful.

**Conclusion**

In conclusion, both LLMLingua and LongLLMLingua are not just
technological advancements; they're culinary metaphors come to life.
LLMLingua acts as the skilled chef, meticulously preparing information
into bite-sized instructions for AI apprentices. LongLLMLingua takes the
baton, transforming into the master chef, seamlessly handling entire
sequences of information like preparing a multi-course feast. Together,
they're revolutionising how LLMs interact with information, paving the
way for a future where:

- AI responses are faster, more accurate, and contextually relevant.
- Machine translation transcends sentence-by-sentence limitations,
 unlocking global communication on a grand scale.
- AI assistants understand our interactions in their entirety, leading
 to more natural and engaging dialogues.

As these technologies continue to evolve, the possibilities are endless.
Imagine AI tutors adapting to your learning style based on years of
educational data, or chatbots understanding months of conversation
history to anticipate your needs with uncanny accuracy.

The future of AI is hungry for efficiency and understanding, and
LLMLingua and LongLLMLingua are serving up the perfect recipe for
success. By empowering LLMs to process information like culinary
masters, they're opening doors to a new era of seamless interaction,
accelerated discovery, and boundless communication.

**Sources**

- LLMLingua research paper: Jiang et al., 2023. LLMLingua: Compressing
 prompts for accelerated inference of Large Language Models. arXiv
 2310.05736v2 <https://arxiv.org/abs/2310.05736>
- LongLLMLingua research paper: Jiang et al., 2023. LongLLMLingua:
 accelerating and enhancing LLMs in long context scenarios via prompt
 compression. arXiv 2310.06839v1 <https://arxiv.org/abs/2310.06839>
- Microsoft LLMLingua page: LLMLingua: Compressing Prompts for
 Accelerated Inference of Large Language Models --- Microsoft Research
 <https://www.microsoft.com/en-us/research/project/llmlingua/>
- Hugging Face LLMLingua Space: LLMLingua --- a Hugging Face
 <https://huggingface.co/spaces/microsoft/LLMLingua>

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