Large language models (LLMs) are trained artificial intelligence (AI) models that understand and create text in a human-like way. Developers, project managers, marketers, and other team members can use large language models to generate and edit software documentation, create presentations, and assist with coding and calculations.
Large language models can significantly boost your company's competitiveness and productivity. Read on to learn more about LLMs, how they work, and the best large language models of 2024. You will also learn how to grow your team with top-rated AI developers.
What Is a Large Language Model?
A large language model is a deep learning algorithm that can spot, summarize, predict, translate, and generate content using vast datasets. A deep learning algorithm is a machine learning (ML) method that identifies objects and performs tasks with increasing accuracy without human intervention.
Many companies and individuals use LLMs to create new content from existing data. This lets them cut costs and time spent on tedious processes and focus on providing better services and products to customers.
Best Large Language Models
Below is a list of the best large language models of 2024, along with each model’s advantages, drawbacks, and real-world applications.
Bidirectional Encoder Representations from Transformers (BERT) is a family of language models introduced by Google in 2018. It was initially implemented in English at two model sizes trained on English Wikipedia (2,500 million words) and the Toronto BookCorpus (800 million words).
BERT uses Transformer, a mechanism that learns contextual relations between words. Transformer has two mechanisms: an encoder for reading text input and a decoder that generates a prediction for the task.
BERT is a powerful LLM for processing language and has low memory requirements. As such, it can be used on various devices. It is also great for smaller and more defined tasks.
BERT’s main advantages are:
- High accuracy for natural language processing (NLP) tasks
- Low memory requirements
- Great for classifying tasks
- Easy to deploy and fine-tune
Unfortunately, BERT is an older LLM model, which means it has several drawbacks. For one, it is not as good as newer LLMs at grasping the context provided in previous sentences. It also lacks robust multilingual support.
The main drawbacks of BERT are:
- Limited context understanding
- Can't handle multiple inputs
- Text generation is lacking
- Limited support for non-English languages
- Fine-tuning can be time-consuming
Since its November 2018 release, BERT has been used by Google, Microsoft, and other companies to help computers establish context by understanding the meaning of ambiguous language.
Specifically, Google uses BERT to boost its understanding of users’ search intent. Meanwhile, Microsoft uses BERT to detect whether a column uses free text or other data types, such as simple numbers or timestamps.
Companies typically use BERT for the following applications:
- Text summarization
- Extracting valuable information from biomedical literature for research
- Finding high-quality relationships between medical concepts
Falcon-40B is an open-source 40-billion-parameter LLM available under the Apache 2.0 license. It is available in two smaller variants: Falcon-1B (one billion parameters) and Falcon-7B (seven billion parameters).
Falcon-40B is trained on casual language modeling tasks, which means it works by predicting the next word. Its architecture is similar to the GPT-3 language model.
Falcon-40B generates a broad range of contextually accurate content. It is also skilled at creating high-quality natural language outputs.
Falcon-40B’s main advantages are:
- Open to commercial and research use
- Uses a custom pipeline to curate and process data from diverse online sources, ensuring the LLM has access to a broad range of relevant data
- Fewer compute requirements
- Provides a more interactive and engaging experience than the GPT series due to being more conversational
As with all things, Falcon-40B has some disadvantages. Compared to many other models, it has an inefficient use of memory. It also comes with the risk of stack overflows and infinite loops.
Other cons include:
- Fewer parameters than the GPT series
- Only supports English, Spanish, German, French, Italian, Polish, Portuguese, Czech, Dutch, Romanian, and Swedish
Many companies and individuals have used Falcon-40B for natural language tasks like understanding and generation, chatbots, and machine translation.
Here’s a list of Falcon-40B use cases:
- Medical literature analysis
- Patient records analysis
- Sentiment analysis for marketing
- Game development and creative writing
3. GPT-3 and GPT-3.5
Generative Pre-Trained Transformer 3 was released by OpenAI in 2020. It was trained on billions of words, making it familiar with human language. Like other LLMs on this list, it's a generative AI model that creates language independently and interacts with users.
GPT-3.5 is an upgraded version of GPT-3 with fewer parameters and a fine-tuning process for ML algorithms. It may provide more coherent and accurate responses than GPT-3. It is estimated to have around 175 billion parameters. As neural network machine learning models, GPT-3 and GPT-3.5 take input text and transform it into what they predict will be the most useful result.
GPT-3 and GPT-3.5 can generate large amounts of text from simple prompts. As a result, teams and individuals will have a much easier time developing new ideas and documentation.
Teams can also use GPT-3 and GPT-3.5 to explain complex concepts. For example, if someone has difficulty understanding a programming concept, they can ask GPT-3 or GPT-3.5 to explain it “as if [the asker] is a 5th grader.” GPT-3 or GPT-3.5 will then explain that concept in simple, lay terms.
GPT-3 and 3.5’s main advantages are:
- Enhanced creativity and productivity
- The basic version is free
- Cost-effective for full usage if you do not have a lot of tasks
- Requires minimal infrastructure
- Minimal staff training
Like other LLMs, GPT-3 and 3.5 aren’t perfect. They may be biased due to the data they were trained with. Using them can also be expensive if you’re on the ChatGPT Pro plan and using it to edit and generate thousands of pages per week.
The disadvantages of GPT-3 and 3.5 are:
- Expensive for high-usage
- May expose sensitive data
- Not continually trained — sources end with 2021 data
- Hallucinations (false information that deviates from contextual logic or external facts)
Many individuals and companies use GPT-3 and 3.5 to generate project ideas. They have also used the two LLMs to create documentation for software development projects.
Companies can also use GPT-3 and 3.5 for the following applications:
- Customer service
- Content creation, including blog posts, long-form content, YouTube video scripts, advertising and marketing copy, and product descriptions
- Virtual assistants
- Risk-rating generation
- Game design and creative writing
GPT-4 is the largest OpenAI GPT model. Unlike its predecessors, it can process and generate both images and language. It also has a system message that lets users specify tasks and tone of voice. As of August 2023, GPT-4 is available in ChatGPT Plus, powers Microsoft Bing search, and will eventually integrate with Microsoft Office products.
Like its predecessors, GPT-4 makes predictions based on input text.
GPT-4 is much more accurate than its predecessors. It also has better reasoning and logical thinking abilities and a sense of humor.
Other advantages include:
- Can generate language and images
- Improved creativity, including the ability to produce appealing and coherent stories
- Cost-effective and scalable
- Can be personalized
Alternatively, GPT-4 is difficult to debug. This is because it generates code based on natural language input. GPT-4 may also perform poorly when asked to do complex tasks — it was designed to create responses based on simple functions.
Other drawbacks include:
- Can generate completely made-up text
- Can't generate entirely new ideas or make predictions
- Requires a lot of training data to generate high-quality code, which can be difficult for small software companies
Companies can use GPT-4 for the same applications as GPT-3 and 3.5, such as coding, translation, content creation, and chatbots.
Orca was created by Microsoft and has 13 billion parameters. It leverages ChatGPT's capabilities and matches up to GPT-3.5 for most tasks.
Essentially, Orca is a smaller version of GPT-4. It uses teacher assistance and progressive learning from GPT-4 to imitate human reasoning.
Although it is still under development, Orca has been shown to outperform other open-source models in several respects, including AGIEval reasoning (a benchmark for evaluating LLMs’ ability to reason about complex topics) and BBH benchmark (a benchmark that evaluates LLMs’ ability to create informative and coherent text).
Other advantages include:
- The progressive learning model lets Orca build upon its knowledge incrementally
- Runs on laptops
- Helps teams learn about complex concepts, such as legal reasoning and financial planning
Orca has several drawbacks. For one, it is still a work in process. Training Orca also requires substantial compute resources, limiting accessibility for some developers and researchers. Finally, running Orca using CPU is slower compared to GPU-accelerated setups. As such, companies may find Orca challenging to use if only CPUs are available.
Companies can use Orca the same way they use GPT-3, 3.5, and 4; to create scripts, content, code, and brainstorm ideas for projects. They can also use Orca to help computers reason about complex topics, such as law, financial planning, and medical diagnosis.
Language Model for Dialogue Applications (LaMDA) is a group of LLMs created by Google Brain. It was initially developed and introduced as Meena in 2020. Bard, Google's experimental AI chat service, was initially based on the LaMDA LLM models.
LaMDA works similarly to BERT and uses a decoder-only transformer language model. It is pre-trained on a text corpus, including dialogues and documents consisting of 1.56 trillion words.
LaMDA has several pros. First, it is a single model, which means it doesn’t have to be re-trained for different subjects or conversations. Other advantages include:
- Can have realistic conversations with users due to training in dialogue
- Continually draws information from the internet
- Specialized for dialogue
Not much is known about LaMDA. However, one major drawback is that only approved teams and individuals can test it.
Organizations can use LaMDA for customer support chatbots, research, brainstorming, and organizing information.
Large Language Model Meta AI (LLaMA) is a language model developed by Meta. LLaMA is now an open-source large language model initially only released to approved developers and researchers. LLaMa model comes in various sizes, including smaller ones requiring less computing power. The largest version has 65 billion parameters.
Like many other LLMs, LLaMA analyzes inputs and predicts the following word.
LLaMA was designed to be efficient and accessible. It is ideal for use cases where resource usage is a vital factor.
Other pros include:
- Many sizes to choose from
- More efficient than many other LLMs
- Less resource-intensive than many other models
One of the main disadvantages of LLaMA is that it may not be as powerful. This is because it was trained on fewer parameters than other big-name models. As such, its answers may be less sophisticated and informative.
Other disadvantages include:
- Limited customization for developers
- Non-commercial license only, which means you can’t use it for commercial applications such as marketing or software development
Companies usually use LLaMA to create chatbots, summarize text, and generate content. They can also use it for research purposes, especially if researchers need to be able to test and train LLM models quickly and effectively.
The Pathways Language Model (PaLM) is a 540 billion parameter transformer-based language model created by Google AI. There are smaller versions of PaLM, including eight-billion and 62-billion parameter models.
Like many popular large language models on this list, PaLM is a transformer model that works by learning to represent the relationships between phrases and words in sentences.
PaLM has many advantages over other large language models, including an efficient training process.
Notably, the model set a new record for training efficiency among LLMs, achieving a staggering 57.8% hardware FLOPs utilization. This was made possible by reconfiguring the Transformer block and parallelism strategy, allowing for simultaneous computation of feedforward and attention layers.
Other pros include:
- Availability in smaller sizes
- Supports over 100 languages
- Powerful code generation and reasoning capabilities
- Seamless Google ecosystem integration
- Controllable outputs — users have more control over the tone, style, and desired outcomes of the generated text
Like other tools, PaLM has several drawbacks. For one, it is less environmentally sustainable than other models due to its large size. Potential biases and ethical problems may also be encoded in the diverse training data used to train PaLM. Finally, PaLM runs slower in informal language tests than Bing and GPT-4. As such, it is not the best fit for people and companies that value efficiency.
Companies can use PaLM for coding, complex problem-solving, calculations, and translations.
StableLM is an open-source model designed for various natural language processing tasks. It is available to anyone to use and modify without any restrictions. Interested developers can download it from GitHub in three- and seven-billion parameter model sizes.
StableLM leverages the power of five cutting-edge open-source datasets specifically created for conversational LLMs, namely DOlly, HH, Alpaca, GPT5All, and ShareGPT. As such, StableLM is better than its peers at long conversations.
StableLM’s main advantage is that it is much better at longer conversations than other models. It is also highly adept at natural conversations, overriding censorship limitations as seen in different models, such as the GPT series, and generating code.
StableLM provides many other advantages, including:
- Open-source language
- Highly customizable
- Includes text-to-image
- Includes a software beta, public demo, and a complete model download for on-premise or cloud use
Despite its unique features, StableLM is generally less powerful and advanced than GPT-3.5 or GPT-4, language models developed by OpenAI. Its answers are less verbose and detailed than GPT-4.
StableLM has several other drawbacks:
- Not good at creative writing
- It may be biased due to being trained on experimental datasets
- Requires technical expertise to set up
Companies usually use StableLM for generating pictures, brainstorming ideas for projects ranging from stand-up comedy routines to YouTube tutorials, coding, and organizing tasks.
Microsoft's latest model is Phi-1. With just 1.3 billion parameters, this small language model has outperformed GPT-3.5. It uses high-quality data sources such as StackOverflow and Stack datasets.
Like BERT and others, Phi-1 is a Transformer-based large language model.
Phi-1 was built for Python coding, making it a great choice for software development teams. It also provides the following advantages:
- High-quality data and responses
- Fewer compute resources due to having fewer parameters
- More diverse and engaging responses than GPT-4
Phi-1 is a very new LLM and still a work in progress. It has several disadvantages, including a lack of domain-specific knowledge of bigger LLMs, such as coding with specific APIs. Additionally, Phi-1 doesn't always understand input errors or style variations in prompts due to having fewer parameters.
Companies can use Phi-1 for chatbots, translation, summarization, and coding.
Grow Your Dev Team With AI Developers
Many large language models have become a great way to boost productivity, especially if you have a small team. However, not everyone can use them effectively and efficiently. Many LLMs, especially ones requiring developers to download and implement them on the cloud or on-premise, can be costly and time-extensive. Using large language models requires specialized AI developers who can download and implement different models from GitHub and HuggingFace.
Revelo can help you hire AI developers who are fluent in LLMs. As Latin America's premier tech talent marketplace, we match companies with time-zone-aligned developers rigorously tested for technical skills, soft skills, and English proficiency. We can also handle onboarding, including benefits administration, payroll, taxes, and compliance, and provide support throughout the developer’s time with your company to ensure engagement and retention.
Interested in growing your dev team with AI developers? Contact us today to leverage AI in your business.