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

#### **Understanding the Balance between Internal Knowledge and External Sources**

<figure>
<img
src="https://cdn-images-1.medium.com/max/1024/0*-GBIM_Ece6r7flKJ" />
<figcaption>Image Generated by Google Gemini</figcaption>
</figure>

### Author

- Qingqin Fang (**ORCID:**
 [0009--0003--5348--4264](https://orcid.org/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. However, integrating contextual knowledge into LLM to keep it
abreast of current affairs and improve response accuracy may lead to
conflicts due to the richness of knowledge sources, known as *knowledge
conflicts*.

So, what is a knowledge conflict? Specifically, it refers to the
contradictory or inconsistent information obtained by LLM from different
sources, including internal knowledge or memory from LLM itself and
external knowledge retrieved through **RAG** (Retriever-Reader
Architecture) or a knowledge graph.

In this post, we primarily investigate the performance of the RAG system
when confronted with inconsistencies between retrieved content and
internal model knowledge. We also explain a detailed analysis of the
fidelity of the RAG model, exploring the conflicts between the internal
priors of RAG and LLMs.

### Background

#### Classification of Knowledge Conflicts

In a recent review by Rongwu Xu et al. on knowledge conflicts, it was
noted that when LLM is used to answer user questions, users may provide
additional prompts, and LLM may also utilize search engines to collect
relevant documents from the web to enhance its knowledge.

<figure>
<img
src="https://cdn-images-1.medium.com/max/1024/0*W471ze_DQMJAakwq" />
<figcaption>Three primary types of knowledge conflicts that an LLM may
encounter, arising from different knowledge sources, Source: Rongwu Xu
et al. 2024. Link: <a
href="https://arxiv.org/pdf/2403.08319.pdf">https://arxiv.org/pdf/2403.08319.pdf</a>.
Contextual (labeled as I. Context, represented by yellow chatboxes) or
inherent to the LLM’s parameters (labeled as II. Memory, represented by
blue chatboxes). When faced with a user’s question (depicted by the
purple chatbox) involving intricate conflicts in knowledge, the LLM must
resolve these disparities to provide precise responses.</figcaption>
</figure>

These user prompts, conversation histories, and retrieved documents
constitute contextual knowledge. With such diverse sources of knowledge,
knowledge conflicts can be classified into three categories:

1\. **Inter-contextual conflict**: Different external documents from
various sources may contain noise or false information, leading to
conflicting information between the retrieved documents.

2\. **Context-memory conflict**: External documents retrieved may
conflict with the parameterized knowledge in LLM.

3\. **Intra-memory conflict**: Users may pose the same question in
different forms, resulting in inconsistent responses from LLM's
parameterized knowledge to different formulations of the question.

### RAG and LLM Knowledge Conflict

In these three conflict patterns, the **context-memory conflict** is the
most extensively studied type. Large language models have fixed
parameter knowledge, which is the result of extensive pre-training. This
static parameter knowledge contrasts sharply with the dynamic nature of
external information, which evolves rapidly.

The core source of context-memory conflict lies in the difference
between contextual knowledge and parameter knowledge. There are two main
reasons for this: **temporal discrepancies** and **contamination with
false information**. Temporal discrepancies refer to the fact that the
latest contextual information is usually considered accurate, whereas
contamination with false information refers to situations where the
contextual information contains erroneous data.

Among the various sources of information, we focus on RAG as an example
to study knowledge conflicts. Before discussing the reliability of the
RAG model, it is essential to understand its functioning. RAG combines
two steps: **retrieval** and **generation**. Firstly, it retrieves
information relevant to the question using a retrieval system, then
provides this information as context to Large Language models in order
to generate more accurate answers.

<figure>
<img
src="https://cdn-images-1.medium.com/max/1024/0*eujuUWXP-gwEy7py" />
<figcaption>Process of generating modified documents for each dataset,
Source: Kevin Wu et al., 2024. Link: <a
href="https://arxiv.org/pdf/2404.10198.pdf">https://arxiv.org/pdf/2404.10198.pdf</a>
Initially, a question is presented to the LLM both with and without a
reference document containing pertinent information related to the
query. Subsequently, this document undergoes perturbation to integrate
modified information and is provided as context to the LLM. The
subsequent observation involves assessing whether the LLM favours the
modified information over its own prior response.</figcaption>
</figure>

For RAG's auxiliary function, our focus lies in: Is providing correct
retrieval content always effective in correcting errors when it's known
that LLM's answers to questions are wrong? Conversely, if the retrieved
content is incorrect, does LLM know to disregard the erroneous
information, or will it repeat the mistake?

### RAG preference rate

In this research, delving into the correlation between the robustness of
the model's prior beliefs and the preference rate of RAG was done. An
intriguing relationship was discovered: the extent to which LLM aligns
with the information furnished by RAG (RAG preference rate) stands in
inverse proportion to the model's confidence in responding without
contextual cues (prior probability). This indicates that LLM's trust in
retrieval information wanes as its inherent prior convictions
strengthen.

<figure>
<img
src="https://cdn-images-1.medium.com/max/1024/0*gxSNNZ-DqdzTVf5P" />
<figcaption>Comparison of RAG preference rate and two factors. Source:
Kevin Wu et al., 2024. Link: <a
href="https://arxiv.org/pdf/2404.10198.pdf">https://arxiv.org/pdf/2404.10198.pdf</a></figcaption>
</figure>

Across six QA datasets with GPT-4, we find a consistent inverse
relationship between RAG preference rate and two factors: the model's
prior response probability and the deviation from that prior. The left
plots show how higher prior probabilities correspond to lower RAG
preference rates, while the right plots demonstrate that greater
deviations result in lower RAG preference rates. These findings
highlight the complex interplay between the model's beliefs, deviation
from those beliefs, and reliance on RAG context.

### Deviation between LLM and RAG

Furthermore, we scrutinized the extent of deviation between prior
knowledge and retrieval data. An experiment was conducted as follows:
First, LLMs were prompted to answer questions without providing any
external context and recorded the model's responses along with their
token probabilities (indicating the model's confidence in its answers).
Then, relevant retrieval content was provided and LLMs were prompted to
answer the same questions again, recording the responses and token
probabilities. Finally, the model's responses were compared between the
two queries. If LLMs provided the same response with and without
retrieval content, it indicated a preference for its internal prior
knowledge. If the response aligned with the retrieval content, it
indicated a preference for RAG information.

<figure>
<img
src="https://cdn-images-1.medium.com/max/1024/0*XzE4KCPvLt6TtblN" />
<figcaption>Examples from three datasets show varied LLM responses to
different types of context modifications. Incorrect responses are
highlighted in red, while correct responses are highlighted in green.
Source: Kevin Wu et al., 2024. Link: <a
href="https://arxiv.org/pdf/2404.10198.pdf">https://arxiv.org/pdf/2404.10198.pdf</a></figcaption>
</figure>

As depicted in this image, when the information retrieved diverges
further from LLM's prior knowledge, the model's reliance on retrieval
information decreases accordingly. This indicates that when the data
supplied by RAG significantly deviates from the model's internal
knowledge, LLM tends to place greater emphasis on its existing beliefs.

### Prompting Techniques

The impact of different prompting techniques (such as strict adherence
and loose adherence) on the model's RAG preference rate was investigated
as well. The choice of prompting technique plays a crucial role in how
the model integrates retrieved information and generates responses.

<figure>
<img
src="https://cdn-images-1.medium.com/max/1024/0*G3ZOl5siAnk-gnkA" />
<figcaption>Effect of different prompts using GPT-4 on RAG preference
rate vs prior probability. Source: Kevin Wu et al., 2024. Link: <a
href="https://arxiv.org/pdf/2404.10198.pdf">https://arxiv.org/pdf/2404.10198.pdf</a></figcaption>
</figure>

In this result, the prompt plays a crucial role in controlling RAG
adherence, with the "Loose" prompt leading to a decrease in RAG
adherence compared to the "Strict" prompt. The "Strict" prompt requires
strict adherence to the retrieved context, while the "Loose" prompt
encourages the model to make reasonable judgments based on the
provided context.

The recent study by Zhuoran Jin arrived at similar conclusions too.
Moreover, it was observed that as the model's capabilities strengthen,
it becomes more self-assured, leading to increased confidence in its own
knowledge as its scale and capacity expand. However, without proper
verification to resolve conflicts, the model remains incapable of
providing accurate responses.

<figure>
<img
src="https://cdn-images-1.medium.com/max/1024/0*KR8Co8C6duLLk3TC" />
<figcaption>Experiment between internal memory and external source on NQ
and TriviaQA. Source: Zhuoran Jin et al., 2024. Link: <a
href="https://arxiv.org/pdf/2402.14409.pdf">https://arxiv.org/pdf/2402.14409.pdf</a></figcaption>
</figure>

As shown in the above table, the experimental results on the NQ and
TriviaQA datasets reveal a significant difference between the recall
metrics when considering knowledge conflicts between internal memory and
external sources. Here, Mem R denotes the recall between predictions and
internal memory predictions, while Con R represents the recall between
predictions and conflicting references. Additionally, IMR --- CMR
indicates the difference in memorization ratio between incorrect memory
and correct memory. The values in bold highlight the highest results.

Through experiments conducted on the NQ and TriviaQA datasets, a notable
trend was observed: after providing conflicting evidence, the recall of
Con R substantially exceeds the recall of Mem R. This finding
underscores the importance of considering external sources of
information when addressing knowledge conflicts, as they can
significantly impact the model's performance.

Furthermore, the presence of the Dunning-Kruger effect within
large-scale models was uncovered. The question arises: does the model
exhibit greater confidence in its correct internal knowledge or in its
incorrect internal knowledge? As the model's capabilities increase, we
found that the memorization ratio of incorrect information surpasses
that of correct information. This indicates that while the model lacks
confidence in its correct internal knowledge, it persists in adhering to
incorrect internal information. This finding aligns with the principles
of human psychology's Dunning-Kruger effect, wherein individuals with
limited abilities tend to overestimate their competence in
specific domains.

Combining the findings from the aforementioned experiments, it can be
surmised that fundamentally, as the capabilities of LLMs grow, they
exhibit increased confidence in their internal knowledge. This
confidence is bolstered by extensive training data and pre-existing
knowledge. However, when faced with conflicting information, LLMs tend
to prioritize their internal knowledge, particularly in the absence of
strict prompting to adhere to retrieved context. This preference for
internal knowledge is evident even as LLMs encounter more complex
conflicts, suggesting a robust reliance on pre-existing beliefs.
Nonetheless, the presence of the Dunning-Kruger effect within these
models indicates a propensity to prioritize incorrect internal knowledge
over correct information, underscoring the intricacies of managing
knowledge conflicts in LLMs.

### Conclusion

In conclusion, the RAG model, as an effective means of improving the
accuracy of LLMs, has garnered widespread attention in practical
applications. Systematic analysis and experiments have revealed the
intrinsic mechanisms of the RAG model when confronted with inconsistent
information, providing important theoretical and empirical support for
the future design and optimization of RAG systems. The findings
emphasize the need to balance internal prior knowledge and retrieval
information when designing RAG systems, as well as the impact of
prompting techniques on model behaviour. These insights are crucial for
ensuring the accuracy and reliability of RAG systems in practical
applications. Additionally, our research highlights the presence of the
Dunning-Kruger effect within large-scale language models, revealing how
these models handle and prioritize knowledge conflicts differently.

Overall, this study contributes to a deeper understanding of the
challenges and opportunities in managing knowledge conflicts within
LLMs, paving the way for advancements in model robustness and
performance.

### **References**

- Xu, R., Qi, Z., Wang, C., Wang, H., Zhang, Y., & Xu, W. (2024).
 Knowledge Conflicts for LLMs: A Survey (Version 1). arXiv.
 <https://doi.org/10.48550/ARXIV.2403.08319>
- Wu, K., Wu, E., & Zou, J. (2024). How faithful are RAG models?
 Quantifying the tug-of-war between RAG and LLMs' internal prior.
 arXiv:2404.10198 \[cs.CL\]. <https://arxiv.org/abs/2404.10198>
- Feng, S., Shi, W., Wang, Y., Ding, W., Balachandran, V., &
 Tsvetkov, Y. (2024). Don't Hallucinate, Abstain: Identifying LLM
 Knowledge Gaps via Multi-LLM Collaboration (Version 1). arXiv.
 <https://doi.org/10.48550/ARXIV.2402.00367>
- Jin, Z., Cao, P., Chen, Y., Liu, K., Jiang, X., Xu, J., Li, Q., &
 Zhao, J. (2024). Tug-of-War Between Knowledge: Exploring and Resolving
 Knowledge Conflicts in Retrieval-Augmented Language Models (Version
 1). arXiv. <https://doi.org/10.48550/ARXIV.2402.14409>

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