Advantages and Disadvantages of Large Language Models

Advantages and Disadvantages of Large Language Models

Large language models or LLMs are not only important in advancing the practical applications of natural language processing but also have made significant contributions to advancing the field of artificial intelligence. Some have also surmised that LLMs have an important role in reaching artificial general intelligence and superintelligence.

It is worth mentioning that these models have also enabled the development and deployment of generative artificial intelligence applications such as text generation, language translation, and code completion. Some models are even multimodal and can process and understand various modalities such as text, images or graphics, audio, and video.

However, despite their central importance in the ongoing artificial intelligence revolution, these classes of AI models have their issues and limitations. The problem with these models also represents one of the main challenges in artificial intelligence. This article lists and explains the advantages and disadvantages of large language models.

Pros of LLMs: Notable Advantages of Large Language Models

1. Generative Applications

The recent wave of generative AI applications that emerged since 2022 are based on LLMs. These include chatbots such as ChatGPT from OpenAI, Bing Chat from Microsoft, and Gemini from Google are based on LLMs. Several image generators and image-to-video generators such as Stable Diffusion and DALL-E also based in part on large language models.

Nevertheless, in considering the examples above, an important advantage of large language models is that they enable the automation of content and data generation. This has several practical applications. Notable use cases include the creation of various forms of content, analyzing large datasets of texts and other modalities, and question answering.

The generative capabilities of LLMs come from rich context handling. These models can analyze and generate content or data with rich contextual understanding and based on the surrounding context. This makes them suitable for a wide range of natural language processing tasks and has put them at the forefront of practical generative artificial intelligence.

2. Scalable and Versatile

Another advantage of large language models is that they are trained using expansive datasets by default. Their model size or parameters determine some of their capabilities. Newer and more advanced LLMs are trained using larger training datasets. Increasing the model size often results in performance improvements in various natural language processing tasks

Some models can be updated and fine-tuned or adapted to newer information and evolving language patterns using new datasets for continuous and incremental improvement. Other models can perform zero-shot and few-shot learning that allows them to understand queries or prompts and produce outputs even when not explicitly trained for them.

A foundation LLM can be developed for various end-use purposes. The foundation model can be fine-tuned into specific derivative models and for various tasks. These include speech recognition, language translation, writing and editing assistance, customer service representation, code interpretation and generation, and digital virtual assistance.

3. Modeling Approaches

There are different approaches to developing large language models. New ones are expected to come up as the subfields of natural language processing, machine learning and deep learning, and artificial neural networks mature further. This fact demonstrates another notable advantage of LLMs because it signifies further innovation and avoids bottlenecks.

One of the most common types of language models is based on autoregressive modeling. The introduction of the transformer architecture has addressed the limitations of autoregressive large language models. A particular LLM can also be modeled using different modeling approaches and architectures or equipped with multiple modalities outside texts.

Several open-source LLMs have been introduced to the public. These models help in promoting to the public the importance of open-source artificial intelligence while also helping in hastening and democratizing the development of AI as a field. This coincides with the growing clamor for effective accelerationism and can help in promoting AI alignment.

4. Information and Interaction

Large language models have the potential to democratize access to knowledge and information in the same manner as the internet and search engines. However, unlike earlier technologies, LLMs can help people better understand complex texts and concepts or those written in another language through their analytical and machine translation capabilities.

General-purpose chatbots have become alternatives to search engines. Chatbots with specialized functionalities can be used in personalized, as mediums for pulling out and explaining a particular knowledgebase, to aid health professionals and patients in knowledge transfer and decision making, and in bridging the gap in business communications.

A large language model can mimic human conversations. It can engage in extended and richer communications. This makes it suitable for applications or use cases that require a human-like interactive experience. The same model also enables humans and other artificial intelligence agents to interact with computer systems using natural language.

Cons of LLMs: Key Disadvantages of Large Language Models

1. Requires Large Datasets

Organizations or individuals who want to develop a large language model need to have access to massive amounts of data. It is important to underscore the fact that the capabilities of a particular LLM depend on the quality and quantity of data it was trained. This is a disadvantage because access to large datasets is limited to large and deep-pocketed organizations.

Leading tech companies such as Google, OpenAI, and Meta Platforms have been criticized for scraping and using data that are available to the public. The classes of data and the extent of scraping remain unclear. Some have noted that the use of supposed public data raises ethical concerns and violates privacy and intellectual property rights.

It is also worth mentioning that a particular LLM needs to be either retrained or fine-tuned to keep it updated and relevant. This is because new data are created and language patterns are evolving. An LLM needs to take these two into consideration to remain useful. This means that developers need to have continuous to newer high-quality datasets.

2. High Computational Cost

Another one of the main disadvantages of large language models is that training and deploying them requires significant computational resources. Remember that LLMs are based on expansive datasets. Processing a huge amount of data necessitates the use of powerful and expensive discrete graphics processing units or dedicated AI accelerators.

More advanced architectures and algorithms such as transformers and recurrent neural networks require higher computational resources. Furthermore, aside from training, the deployment of large and advanced LLMs consumes significant computational resources and power during inference or when producing outputs and during end-use operations.

The aforementioned is a problem because it bars smaller organizations from developing and deploying their own large language models while limiting the development of these models to a select few. This also narrows down the options of end users. It is also important to note that the computational requirements also translate to environmental costs.

3. Bias Potential and Hallucination

A particular LLM has the potential to reflect or augment the biases of its training dataset. This can lead to the model generating outputs that are offensive or prejudiced toward certain groups and cultures. It is important for developers to secure huge amounts of data, evaluate these data for potential biases, and align the model to reflect desired values and goals.

The leanings to hallucinate is also another disadvantage of large language models. Hallucination in artificial intelligence is a phenomenon in which a particular AI model or AI system produces outputs that appear accurate or reliable but are not real or based on data. Advanced chatbots such as ChatGPT and Google Gemini have been shown to hallucinate in several instances.

Another limitation of an LLM is that it struggles to solve complex problems, design complex plans, analyze large or expansive bodies of text, and understand advanced mathematical problems. It might produce outputs that are confabulated or information and answers that sound confident and true but are deceptive or misinformed when inspected.

4. Unforeseen Consequences

The expanding usage and growing acceptance of large language models have raised various concerns over their risks or potential to produce unintended consequences. An overreliance on generative applications such as chatbots for tasks like writing and research, content creation, data evaluation, and problem-solving can hamper critical and creative thinking.

Several observers have grown concerned over the likelihood of these models to affect certain occupations and render professionals unemployed. Sophisticated LLMs can automate various tasks that often require human involvement. Examples include writing, programming, graphics design, schedule management, data processing, and customer service.

Another disadvantage of large language models is that they create novel and unique opportunities for supplementing malicious practices. These include spamming and bot activities, creating deepfakes, disseminating misleading information, manipulating public opinions or spreading propaganda, targeted surveillance and monitoring, and phishing and scams.

Rundown: The Pros and Cons of Large Language Models

The advantages of large language models have made them one of the most relevant and versatile products emerging from the field of artificial intelligence. LLMs have realized several practical applications of natural language processing and have encouraged a more positive adoption of AI technologies. The arrival of advanced chatbots, reliable speech recognition applications, and other generative applications are creating new opportunities across various levels and facets of modern society. However, like other technologies, the disadvantages of large language models have also created new risks and have the potential to disrupt prevailing norms.