Advantages and Disadvantages of Large Language Models and Small Language Models

1.     Introduction

Large language models (LLMs) have emerged as a frontier technology in Artificial Intelligence (AI) systems that can perform complex tasks like text translation summarization and retrieval of information. These developments are driven by the availability of large-scale training datasets and advances in computational capabilities. However, a new trend is emerging characterized by high demand for highly efficient language models that can be fine-tuned with task instruction data and aligned with user preferences. Although the choice for a language model is between the different features and capabilities of LLMs and Small Language Models (SLMs), specific domains and task requirements influence the ideal application.

2.     Overview of Language Models

2.1 Large Language Models

A Large Language Model (LLM), which is also called a transformative or next-generation language model, is a Machine Learning (ML) model that uses Artificial Intelligence (AI) and Natural Language Programming (NLP) techniques to perform a wide range of tasks (Nicholas & Bhatia, 2023). The most common LLMs are used for building chatbots like OpenAI’s ChatGPT. Leading global technology companies like Microsoft, Google, and Meta have integrated LLMs into their services and products, such as Google’s PaLM and Meta’s LLaMa. These LLMs typically scan large volumes of text to learn the context of words and sentences. The models can perform various tasks using the learned syntax, such as sentiment analysis, text generation or summarization, language translation, and hate speech detection. The early development of LLMs could be traced to the development of language models and neural networks (NNs) (Hadi et al., 2023). The development of Recurrent Neural Networks (RNNs) enabled the modeling of sequential data, including language. Yet, RNN was limited by challenges like long-term dependencies and vanishing gradients (Hadi et al., 2023). The development of the Transformer architecture allowed for efficient handling of long-term dependencies and laid the foundation for LLMs. Since then, LLMs have evolved becoming more complex and gaining advanced capabilities. Current LLMs can be categorized into five classes; training, inference, evaluation, applications, and challenges (Naveed et al., 2023).

Figure 1. LLMs are divided into branches (adapted from Naveed et al., 2023).

Although LLMs can handle complex tasks like machine translation and content creation, they also have their limitations. For example, they are computationally less efficient, they are less adaptable to certain applications, they require advanced hardware, which comes at an additional cost, and consume more energy.

2.2 Small Language Models

One of the recent developments in language models is the focus on efficiency, customizability, and cost. Consequently, there is a shift towards AI systems that use relatively smaller model sizes. These systems are known as Small Language Models (SLMs) and typically have less than 100 million parameters. Smaller model sizes enhance efficiency, cost-effectiveness, and adaptability compared to LLMs. Computational efficiency is attributed to having fewer parameters. Efficiency advantages directly lead to cost savings. Since SLMs use fewer parameters, they are highly adaptable to specialized applications, such as mobile applications and Internet of Things (IoT) devices. Yet, SLMs have limited language capability due to reliance on a less diverse knowledge base.     

3.     Comparison of Advantages and Disadvantages using Case Studies

3.1 Comparison Criteria

There are various parameters to consider when comparing LLMs and SLMs. One of the key factors is model size and computational complexity. Model size refers to the number of parameters the model can accommodate (Lappin, 2023). The size of a language model depends on the empirical relationship between the number of learnable parameters, the size of training data, and the number of operations to train the model (compute size) (George, 2023). Model complexity refers to the complexity of the function that is to be learned. Another key parameter is performance and efficiency. Various performance metrics can be used to compare language metrics, such as accuracy on domain tasks and applications. Language models can also be compared based on the perplexity of test data, which quantifies the accuracy of the model in predicting texts. Lower perplexity values indicate better model performance. Other performance metrics include word error rate and accuracy (see Figure 2) (Chen et al., 2008).

Figure 2. Illustrating word-error rate vs perplexity metrics for language models (Chen et al., 2008).

Accuracy quantifies the proportion of correct predictions out of the total instances. On the other hand, training and data requirements refer to the requirements for fitting the weights and biases into the model during development. Various organizations have also created best practices and guidelines for deploying language models. The deployment requirements for LLMs and SLMs may differ depending on the programming framework and cost implications. The key metrics for model deployment include memory and CPU utilization, and ease of troubleshooting; Table 1 provides a summary of the key parameters to consider when comparing the advantages of LLMs and SLMs.

Table 1. Parameters to consider when comparing language models.

Criteria

Description

Size and computational complex

Number of parameters and the complexity of the function to be learned.

Training & data requirements

The time and data requirements during model training.

Deployment requirements 

Deployment requirements of language models

Performance & efficiency

The accuracy of the model in predicting or accomplishing a given task.

Customizability and accessibility

The ability to adapt language models to new domains and tasks.

Security and intellectual property

Security and privacy threats and potential IP and legal issues.

Cost implications

The cost of designing, developing, implementing language models.

3.2 Comparison Results

Size and computational complexity

In terms of size, LLMs can have intricate architecture comprising deep NNs with billions of parameters (Al Zubaer et al., 2023). The primary components of LLMs include the decoder block of the transformer architecture and the next-token prediction objective for training the model (Al Zubaer et al., 2023). For example, GPT-3 has a parameter size of 175 billion while ESMFold (Meta AI) has a size of 15 billion.  The multiple NN layers create a complex architecture comprising deep learning models trained on large amounts of data. On the other hand, SLMs consist of lightweight architectures with less than 10-15 million parameters. Comparisons based on computational requirements show that LLMs require numerous GPU processors whereas SLMs use a small number of processors.

Training and data requirements

LLMs like chat boxes can be trained on vast datasets, including conversational data to have the capacity to generate natural language. During training, the model is given a sequence of words and trained to predict subsequent words in the sequence in a process that is repeated numerous times until satisfactory performance is achieved. The process of training LLMs can take weeks to months to meet the diversity of training datasets. Unlike LLMs, SLMs can be trained on smaller datasets suitable for specific tasks. The training process for SLMs can be completed within a few hours to days.

Deployment requirements

Deployment of LLM requires extensive hardware infrastructure to provide sufficient computational power. LLMs are suitable in resource-intensive environments. On the other hand, SLMs are lightweight, which indicates ease of deploying in resource-constrained environments, such as IoT contexts. SLMs can be deployed on standard hardware and infrastructure, which ensures broad accessibility.

Performance and efficiency

LLMs focus on handling complex and diverse tasks with a high level of accuracy compared to SLMs, which handle only small tasks (Al Zubaer et al., 2023). Comparison based on perplexity shows that LLMs have superior perplexity than SLMs, which suggests that the latter has better quality in terms of stochasticity (Serrano et al., 2023). Figure 3 shows a comparison of the performance of LLMs on certain tasks. No key trends were identified in the specific behaviors.

Figure 3. Comparing the performance of LLMs on specific tasks shows no discernable trends (Bowman, 2023).

Customizability and accessibility

Customizing LLMs for any specific task would require substantial resources, including specialized hardware. The resources may not be available or the changes may lead to insurmountable technical and economic challenges. In contrast, SLMs are easy to customize to specific applications and tasks without spending too much on hardware and other infrastructure resources. SLMs are also compatible with multiple application domains and tasks across different applications.

Security and IP rights

The vast amount of data and training requirements of LLMs implies potential intellectual property (IP) rights issues and murky legal implications (Neelbauer & Scheidel, 2023). According to Li et al. (2023), LLM-based code generation poses a risk of IP theft via imitation attacks. Moreover, LLMs pose potential security risks due to large attack surfaces associated with their architectures. These algorithms mimic human intelligence, which poses concerns of data poisoning, user privacy concerns, and security risks. Six key security risks have been identified: discrimination, hate speech, misinformation, malicious use, social and environmental damage, and human-computer interaction issues (Weidinger et al., 2022). SLMs rely on small-scale data and their training is not as extensive. The attack surfaces of SLMs are also limited by the smaller architecture.

Cost savings

Since LLMs require extensive hardware and infrastructure, they are considered more costly to implement than SLMs. The high computational resource requirements for training and deploying LLMs translate into cost overheads. Research shows that LLMs get more capable with an increase in investment (Bowman, 2023). The advantage of LLMs is that they automate complex tasks and improve creativity. However, SLMs have lower operational and development costs, which makes them more accessible.

Table 2. Comparison of SLMs and LLMs

Criteria

Small Language Models

Large Language Models

Size and computational complex

Lightweight architecture with < 15 million parameters.

Complex architecture with billions of parameters.

Training & data requirements

Takes a few hours to days and requires small training datasets.

Takes days to months and requires large training datasets.

Deployment requirements 

Easy to deploy in resource-constrained environments like mobile applications and IoT devices.

Deployment requires extensive hardware infrastructure, including robust GPUs with the required computational power.

Performance & efficiency

Handles simple tasks. Low perplexity.

Handles complex tasks. High perplexity.

Customizability and accessibility

High customizable to specific applications.

Limited customizability.

Security and intellectual property

Small architecture with limited security threats and IP issues.

IP and security risks due to extensive use of data and training.

Cost implications

Affordable and accessible.

Extensive hardware and infrastructure requirements come with additional cost implications.

4.     Discussion of Findings

Both LLMs and SLMs have their unique advantages and drawbacks, which influence their suitability for specific applications.  In this work, the advantages and disadvantages of LLMs and SLMs were examined based on seven areas or factors that are likely to influence the choice of a language model. Overall, the findings show that the advantages of LLMs include the ability to understand complex tasks due to training on vast and diverse data, advanced NLP capabilities, and versatility for a wide range of tasks. While LLMs are robust; they have limitations. The complex architecture means that they need extensive training data. Although some LLMs allow fine-tuning on specific tasks, these language models are largely customizable or require substantial resources to adapt to new applications. They also require extensive hardware and infrastructure investments, which can lead to high operational costs. Security risks IP rights issues and potential data bias also can affect the deployment of LLMs. According to Lappin (2023), a careful assessment of the architectures of LLMs is needed to determine their potential and limitations.

SLMs also have unique features. Their advantages could be attributed to their lightweight architecture, compact size, versatility, and high performance. Training SLMs takes a shorter time than training LLMs. The small architecture also implies fewer security and privacy risks, minimal IP issues, and enhanced customizability and adaptability. SLMs are also highly accessible and affordable for different user types. However, SLMs have some drawbacks, like limited NLP capabilities and inability to work on complex tasks.

5.     Conclusion

Language models are rapidly evolving, with both slam and large language models playing key roles in diverse applications. To choose between SLMs and LLMs, a user must understand their features, their unique strengths and weaknesses, and the specific requirements of their tasks. LLMs are ideal in resource-endowed situations that require handling complex tasks. On the other hand, SLMs are suitable for resource-constrained environments where accuracy and lightweight application are a priority, such as IoT devices, mobile applications, and edge networks.  Further research should evaluate the applicability of language models in specific domains or tasks.

References

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