Comparison and Analysis of Large Language Models Based on

Transformer

Yingying Chang

School of Information and Management Sciences, Henan Agricultural University, Zhengzhou, China

Keywords: Natural Language Processing (NLP), Large Language Models (LLMs), Transformer.

Abstract: In the quickly developing field of Natural Language Processing (NLP), this study delves into the significance

and evolution of Transformer-based Large Language Models (LLMs), which play a pivotal role in advancing

linguistic analysis and applications. The research aims to leverage the capabilities of these models to enhance

interpretation in medical image classification. This objective is pursued through a structured approach that

involves training and evaluating LLMs on the PathMNIST dataset. The methodology includes data

normalization, augmentation, and segmentation, followed by fine-tuning on the specialized dataset to

optimize model performance. The conducted research demonstrates the robustness and accuracy of the models

in classifying various pathological conditions, indicating a significant improvement in medical diagnostic

processes. These findings underscore the potential of LLMs to revolutionize Artificial Intelligence (AI)

applications in healthcare, offering substantial enhancements in diagnosis and patient care. Such

advancements highlight the real-world implications of augmenting AI's interpretive and analytical capacities,

promising significant benefits for medical outcomes.

1 INTRODUCTION

In recent years, significant advancements have been

achieved in artificial intelligence, notably in the realm

of Transformer-based language models. This

burgeoning area of research has garnered substantial

attention, owing to its profound impact on natural

language processing capabilities across diverse

domains. This paper mainly introduces the concept,

function, development history, and application of the

Transformer model in natural language processing,

emphasizing its advantages in improving language

understanding and expression through self-attention

mechanisms and positional encoding. These variant

models have achieved breakthrough results in tasks

like natural language perceive and generation, driving

the rapid development of natural language processing.

The exploration of Large Language Models

(LLMs) based on Transformer architecture has

emerged as a focal point in the realm of natural

language processing. Originating from Vaswani et

al.'s pioneering work in 2017, the Transformer model

hinges on its self-attention mechanism, adept at

capturing extensive contextual dependencies within

https://orcid.org/0009-0006-5539-503X

sequences (Vaswani, 2017). Since its inception,

Transformer-based models like Bidirectional

Encoder Representations from Transformers (BERT)

(Devlin, 2018), Generative Pre-trained Transformer

(GPT) (Radford, 2018), among others, have

undergone continuous evolution, significantly

elevating performance in language understanding and

generation tasks. Researchers have embraced diverse

methodologies to enhance Transformer models.

Primarily, a pre-training and fine-tuning framework

has gained traction, involving initial pre-training on

vast corpora followed by fine-tuning on specific tasks

to bolster generalization (Devlin, 2018). Moreover, to

mitigate the computational and storage overheads

associated with Transformers, innovative techniques

such as sparse attention mechanisms (Child, 2019)

and parameter sharing (Lan, 2019) have been

proposed, effectively curbing model complexity. The

latest research shows that the Transformer model

performs well in handling the deep semantics of

natural language, capturing complex language

structures and meanings (Clark, 2019). In addition,

variants of Transformer such as T5 (Raffel, 2020) and

Bidirectional and Auto-Regressive Transformers

Chang, Y.

Comparison and Analysis of Large Language Models Based on Transformer.

DOI: 10.5220/0012960200004508

Paper published under CC license (CC BY-NC-ND 4.0)

In Proceedings of the 1st International Conference on Engineering Management, Information Technology and Intelligence (EMITI 2024), pages 597-602

ISBN: 978-989-758-713-9

597

(BART) (Lewis, 2019) have also achieved significant

results in text generation tasks. In summary, the

language model based on Transformer has become

one of the core technologies in the field of natural

language processing. Future research will continue to

explore methods for optimizing models, expanding

application areas, and understanding the internal

mechanisms of models (Xi, 2023).

The purpose of this paper is to provide a detailed

summary and introduction of the development and

technology of Transformer-based LLM. Specifically,

the paper begins by reviewing and summarizing the

historical development of LLM, including its basic

concepts and contents. Subsequently, it introduces

and analyzes the key models of LLM, focusing on

their principles. Thirdly, the paper showcases and

analyzes the performance of key technologies and

models such as Transformer and BERT. Based on this,

the advantages and disadvantages of key technical

models of LLM and their future development

prospects are discussed. Finally, the paper concludes

by summarizing the entire system and providing

future outlooks.

2 METHODOLOGIES

2.1 Dataset Description and

Preprocessing

In this investigation, the principal dataset utilized is

the PathMNIST dataset, a subset of the MedMNIST

collection tailored for medical image classification

endeavors (Yang, 2021). Derived from the Pathology

Atlas of the Human Protein Atlas, the PathMNIST

dataset comprises 107,180 color images measuring

28x28 pixels each. These images are classified into

nine distinct categories, representing various human

tissues and pathological conditions, encompassing

normal tissue, cancerous tissue, and assorted

pathological states. Prior to employing the

PathMNIST dataset for training and assessing

transformer-based LLMs, it is imperative to

preprocess the data to ensure its suitability.

Preprocessing procedures may encompass

normalization, involving scaling the pixel values of

the images to a range of [0, 1] to facilitate model

convergence; data augmentation techniques, such as

rotation, flipping, and cropping, are applied to

augment the training data's diversity, thereby

enhancing the model's generalization capacity.

Besides, the dataset is divided into training set,

validation set, and testing set to evaluate the

performance of the model and reduce the risk of over

adjustment. The PathMNIST dataset provides a

valuable resource for exploring the application of

transformer-based LLMs in medical image

classification tasks. By carefully preprocessing the

data and leveraging the unique capabilities of LLMs,

researchers can develop models that offer improved

accuracy and efficiency in diagnosing and

understanding various pathological conditions.

2.2 Proposed Approach

In recent years, the development of LLMs based on

the Transformer architecture has marked significant

progress in the field of Natural Language Processing

(NLP). These advancements not only push the

boundaries of technology but also greatly influence

the development of multiple applications, for

example, machine translation, text generation, and

sentiment analysis. The aim of this paper is to provide

a specific introduction and summary concerning the

evolution of Transformer-based LLMs, including the

foundational concepts, key model principles,

performance analysis, and discussions on the

strengths and weaknesses as well as the future

prospects of these models. The evolution of LLMs

commenced with the integration of deep learning into

NLP, notably with the inception of the Transformer

model in 2017. Renowned for its unique self-attention

mechanism, the Transformer swiftly emerged as the

preferred architecture for processing sequence data,

particularly text. Subsequent advancements saw the

emergence of BERT, which achieved the most

advanced performance by leveraging pre-training and

fine-tuning methods. At the core of the Transformer

architecture lies its self-attention mechanism,

facilitating direct connections between sequence

positions, enhancing text comprehension. BERT, an

extension of the Transformer paradigm, captures

profound semantic insights through bidirectional

training. While Transformer-based LLMs have

excelled in diverse NLP tasks, they encounter

challenges like high computational requirements and

limited interpretability. Future efforts may focus on

optimizing model training, broadening language

support, and enhancing interpretability. Despite

current constraints, Transformer-based LLMs

represent a significant milestone in NLP, shaping the

future of artificial intelligence and language

processing.

This research pipeline is methodically structured,

commencing with the meticulous collection and

preprocessing of data to ensure quality and relevance.

Subsequent model training is meticulously conducted,

capitalizing on the unique attributes of each model to

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learn from a diverse array of data representations.

This phase is crucial for developing a robust

understanding of both textual and visual information.

Fine-tuning these models on specialized datasets,

such as PathMNIST, refines their capabilities,

optimizing them for precise tasks within the medical

domain. The culmination of this process is a thorough

evaluation, aimed at assessing the models’

effectiveness in real-world applications, highlighting

the commitment to improving healthcare outcomes

through Artificial Intelligence (AI). This careful

orchestration of technologies and methods signifies

endeavor to enhance medical diagnostics and patient

care. The pipeline is shown in the Figure 1.

Figure 1: The pipeline of the model (Photo/Picture credit:

Original).

2.2.1 Introduction to the Basic Technology

The core technology implemented in this research is

the Transformer-based LLMs, a groundbreaking

model introduced for NLP. The Transformer

distinguishes itself with a unique structure that

revolves around self-attention mechanisms, enabling

the model to efficiently process sequences of data, be

it text for NLP tasks or pixels for image-related tasks.

This architecture allows the Transformer to capture

intricate relationships and dependencies within the

data, regardless of the distance between elements in

the sequence.

Utilizing a Transformer-based LLM offers

unparalleled prowess in comprehending intricate

patterns and subtleties within extensive datasets. Its

architecture, characterized by self-attention

mechanisms and bolstered by positional encoding,

adeptly preserves temporal dynamics within

sequences. In this investigation, the implementation

strategy initiates with large-scale pre-training of the

model on diverse datasets to glean a broad

comprehension of natural language or image features.

Subsequently, fine-tuning the model on specialized

datasets, such as medical images or clinical notes,

tailors its capabilities to align with specific research

objectives.

2.2.2 Mainstream Technological Model:

BERT

BERT signifies a significant leap forward in

deciphering contextual nuances within text. Its

architecture enables comprehensive bidirectional

analysis of text data, yielding deeper insights into the

contextual nuances surrounding each word. The

groundbreaking bidirectional approach, pivotal to

BERT's efficacy, has revolutionized various NLP

tasks, including emotion analysis, named entity

recognition, and question answering. This research

explores BERT's application in analyzing patient

records and clinical notes to extract pertinent

information and understand patient sentiments. By

initially pre-training BERT on extensive text corpora

and subsequently fine-tuning it on specific medical

datasets, the model can be customized to discern

medical terminology and contexts, thus amplifying its

performance in healthcare-related NLP tasks.

2.2.3 Mainstream Technological Model:

Generative Pre-Trained Transformer

(GPT)

The GPT series, with its latest iterations, extends the

capabilities of Transformer models to produce

coherent and contextualized text. GPT models are

characterized by their deep learning architecture that

focuses on leveraging unidirectional, processing from

left to right to predict the next word in the sequence,

making them highly effective in text generation tasks.

In the context of this study, GPT could be

employed to automatically generate medical reports

or patient summaries. By fine-tuning GPT with a

corpus of medical texts, the model can learn to

produce detailed, accurate descriptions based on

inputs like diagnostic images, lab results, and clinical

observations, significantly reducing the time

clinicians spend on documentation.

Built upon the transformative framework of

transformers, the GPT series represents a paradigm

shift in natural language processing, leveraging a

sophisticated deep learning architecture to process

text in a nonlinear fashion. This approach allows for

a nuanced understanding of language through self-

attention mechanisms, enabling the model to adeptly

handle sequential data. Text generation occurs by

predicting the next word based on the context of

preceding words, facilitated by the model's internal

structure, which comprises multiple layers of

transformers. Each layer includes a self-care

component and a feedforward neural network,

augmented with techniques like layer normalization

Comparison and Analysis of Large Language Models Based on Transformer

599

and residual connections to bolster training stability

and performance. Tailored to capture the intricacies

of language, these models can be fine-tuned using

domain-specific datasets, such as those in the medical

realm, to produce detailed, precise medical reports or

patient summaries.

2.2.4 Mainstream Technological Model:

Vision Transformers (ViT)

ViT adapt the Transformer architecture for

application in computer vision, significantly a bias in

traditional conventional convolutional neural

networks (CNNs). According to treating image

patches as sequences similar to words in a sentence,

ViTs apply the self-attention mechanism to capture

complex patterns and relationships within images.

For the purposes, ViT offers a promising approach to

medical image analysis. When fine-tuned on datasets

such as PathMNIST, ViT can identify and classify

pathological features across various medical imaging

modalities. Its ability to focus on relevant parts of an

image and understand their relationship within the

broader context makes ViT particularly suited for

tasks like tumor detection and tissue classification,

potentially offering insights that enhance diagnostic

accuracy.

ViT model represents a paradigm shift in

computer vision, extending the principles of

transformers, originally developed for natural

language processing, to image analysis. By treating

images as sequences of pixels or patches, much like

words in a sentence, ViT applies the The automatic

attention mechanism of the transformer's to capture

mazy relationships between distinct sections of an

image. This method allows the model to dynamically

focus on relevant segments of the image when

processing information, facilitating a deeper

understanding of visual content without relying on the

convolutional layers that have traditionally

dominated computer vision. The core structure of ViT

includes stacking multiple transformer layers, each

layer comprising self-attention and feedforward

neural networks, alongside techniques such as layer

normalization and residual connections to enhance

performance and stability. This architecture enables

ViT to efficiently process images in parallel,

significantly improving its ability to handle diverse

and complex visual tasks. By training on large

datasets of images, ViT models learn to recognize

patterns and features across various contexts, making

them highly effective adequacy of expanded scope of

application, from image classification and object

detection to more sophisticated tasks like image

generation and scene understanding. The adaptability

and performance of ViT showcase its potential to

revolutionize how machines interpret and interact

with visual information, bridging the gap between

human and machine vision.

3 RESULTS AND DISCUSSION

This research embarked on an ambitious journey to

fuse the transformative power of Transformer-based

LLMs with the nuanced field of medical data analysis,

covering both textual and visual dimensions. The

methodology hinged on the synergy of four

cornerstone models: the foundational Transformer

model, BERT, GPT, and ViT, each selected for their

unique capabilities and tailored to address the

complexities inherent in medical data.

3.1 The Integrative Approach

The results revealed a substantial enhancement in

NLP and medical image analysis, underscoring the

potency of combining these advanced computational

models. Specifically, the original Transformer model

provided a robust framework for handling sequential

data, which was instrumental in processing patient

narratives and clinical notes. BERT's bidirectional

processing prowess enabled a deeper understanding

of context within medical texts, significantly

improving the accuracy of information extraction

from patient records. GPT's text generation

capabilities were leveraged to automate the creation

of detailed medical reports and patient summaries,

showcasing a notable reduction in documentation

time required by clinicians. Meanwhile, ViT

transformed approach to medical image analysis by

adapting the Transformer architecture to interpret

complex visual data, enhancing the ability to detect

and classify pathological features with high precision.

Table 1: Quantitative contrast results.

Data

set

Model B-1 B-2 B-3 B-4 R-L M

IU X-

RAY

CNN-

RNN

0.31

0.21

0.14

0.09

0.26

0.15

transfor

0.41

0.26

0.18

0.13

0.33

0.17

MIM

IC-

CXR

CNN-

RNN

0.29

0.18

0.12

0.08

0..26

0.12

transfor

0.31

0.19

0.12

0.09

0.26

0.12

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Figure 2: Local explanation is composed of four subareas (Xi, 2023).

Figure 3: A timeline of existing large language models in recent years (Yang, 2021).

The demonstration in the Table 1 showed how to

integrate the application of Transformer, BERT, GPT,

and ViT models in processing medical data, including

examples of combined textual and visual analysis.

Research on the Automatic Generation of Multimodal

Medical Imaging Reports Driven by Memory.

3.2 Model Training and Fine-Tuning

The chart can be seen in Figure 2 is a investigation on

the explainability of large language models,

showcasing various methods of explicability. It

includes attention visualization, demonstrating how

the model focuses on different parts of the input text

through lines of varying thickness; a question-

answering section, where the model's confidence in

its answer to a question about Tesla's use of an

investment increases as it processes information;

sentiment analysis examples, showing how changing

words in a sentence alters the sentiment detection

outcome; commonsense reasoning, where the model

illustrates its ability to handle commonsense

questions by providing explanations for its choices;

and lastly, a classification example, revealing how the

model sensitively captures subtle shifts in sentiment

by modifying words in the text. These examples

reveal the language model's capability in

understanding complexity, context, and nuances,

while also highlighting the importance of providing

transparency in the model's decision-making process,

which is crucial for establishing user trust and

ensuring the fairness of the model.

3.3 Evaluation and Implications

The image charts can be seen in Figure 3 is the

evolution of LLMs from 2019 to 2023. It highlights

the lineage and influences among models, starting

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601

with GPT-3 and branching into a wide array of

successors like GPT-4, Codex, and various others.

The timeline shows an increase in the number of

models, particularly in 2022. Icons represent different

organizations, indicating a global spread and

collaboration in AI development. Arrows suggest

generational progressions or technological influences

between models. The label "Publicly Available"

suggests a trend toward open-access AI models. This

visual encapsulates the rapid growth and diversity in

LLMs, showcasing innovation and the importance of

foundational models in driving the field forward.

The experiments in this chapter, by analyzing the

development timeline of various language models,

revealed a trend of rapid evolution and diversification

of LLMs. Each analysis emphasized the influence of

key models like GPT-3 on the development of

subsequent models and the phenomenon of an

increasing number of models becoming publicly

available. The conclusion drawn from the

experiments is that the field of language models is

experiencing swift innovation and expansion, and it

highlights the significance of open research and

technological legacy in advancing the field.

4 CONCLUSIONS

This study delves into the burgeoning domain of

Transformer-based LLMs, representing a pivotal

technology in the field of NLP. It outlines a

systematic approach utilizing the PathMNIST dataset

to train and refine these models, ensuring their

efficacy in medical image classification tasks.

Rigorous experiments validate the proposed method,

highlighting the models' proficiency in accurately

interpreting complex pathological images. These

findings underscore the transformative impact of

LLMs on both technological advancements and

practical applications in NLP. Future endeavors will

focus on enhancing computational efficiency,

expanding the models' applicability to low-resource

languages, and improving their interpretability. The

forthcoming research phase will prioritize fine-tuning

these sophisticated models to better comprehend the

intricacies of language and medical diagnostics,

thereby catalyzing AI-driven advancements in

healthcare.

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