In today’s world, Large language models are highly advanced programs that help machines understand and generate Human-like text. They have been the foundation of Natural language processing for almost 10 years now.
Although artificial intelligence ( AI ) has gained popularity recently, large language models ( LLM ) started to appear around 2014 after the discovery of the publication of a research paper “Neural Machine Translation by Jointly Learning to Align and Translate.”
So what are LLMs? Well, Large language models ( LLM ) are a type of language model that is known for their ability to understand general-purpose language and generation. LLM gets these abilities by using data to learn and train on billions of parameters which also consume large amounts of computational resources during the process. LLMs are basically Artificial neural networks otherwise well known as transformers and are pre-trained using methods like self-supervised learning and semi-supervised learning.
After the Discovery of the publication, LLM has seen a giant boost in research and development over the years with LLMs like ChatGPT from OpenAI, Bard from Google, PaLM, Claude, Cohere, Flacon, LLaMA by Meta, Guanaco, MPT, Lazarus and many more emerging today. Some of them are open source while others are closed source from large corporations such as Google, Microsoft, X formally known as Twitter, ChatGPT by OpenAI, and many more.
In this article, we will dive deep into the best LLM currently accessible and why they have an edge over others.
12 Best Large Language Models ( LLMs ) in 2023
ChatGPT is an Artificial intelligence chatbot also known as Generative pre-trained transformers ( GPT ) is a type of learning model that is used to generate human-like text, which is commonly used to answer questions, summarize and translate text, generate codes, blog posts and much more.
ChatGPT-4 or GPT-4 is the most latest and advanced language model introduced by OpenAI on March 14th, 2023. It has shown the capability of human-level performance in various academic exams.
Compared to its predecessor GPT-3.5, GPT-4 shows drastic improvements over the natural language processing ( NLP ) capabilities via increased accuracy as GPT-4 is trained on 8 models with 220 billion parameters each which in total amounts to 1.76 trillion parameters which, by far is the highest amount of Parameters on which an LLM has ever trained. However, it is a closed-source model which makes it very difficult to edit or modify.
1. It’s a time saver as ChatGPT 4 is consistent and reliable by being fast and accurate to the queries users input. As ChatGPT-4 works 24×7 it becomes a reliable source of information at ease.
2. It’s cost-effective and scalable as it can handle large amounts of tasks at once and can automate a majority of the tasks thrown at it as it helps businesses to scale cost-effectively and provide efficiency.
3. The best part about ChatGPT-4 is that it can be tailored according to the needs of its users as it uses its Artificial intelligence models which are trained on the latest scale of parameters to ensure the diversity of its users.
4. ChatGPT-4 is multilingual which removes language barriers for its users around the world, as it uses a system that allows it to connect better with the users to deliver and bridge linguistic barriers.
1. ChatGPT-4 has gained a reputation for providing wrong answers as it stands out compared to other AI tools cause of its unique way of approaching responses.
2. ChatGPT-4 has been revealed to be extremely biased, as ChatGPT-4 was trained on the largest amount of parameters its AI model was created from the collective writings of humans which has resulted in ChatGPT’s biggest flaws as it also includes some of the same biases that exist in our human world.
3. ChatGPT-4 is a harmful tool in the wrong hands, from recent discovery ChatGPT-4 was used to conduct malicious cyber activity despite its improvements over time.
4. ChatGPT-4 can manipulate humans to perform certain tasks, as this was recently discovered by the Alignment Research Center ( ARC ) when conducting research they found out that ChatGPT-4 Acted as a visually impaired person and interacted with humans to conduct tasks like solving Captcha puzzles.
GPT-4 can be used to its full potential for website creation in terms of dynamic content creation, design prompts, and interactive content. It can be even used for monetization by targeted advertising, and personalized user experience. And it can be used for Marketing like influencer collaborations, and video marketing.
ChatGPT also known as Generative pre-trained transformers (GPT ) GPT-3.5 the predecessor of GPT-4 both introduced by OpenAI. ChatGPT-3.5 was officially released to the public on Match 15th 2022. It has a faster response time but at the cost of accuracy cause of its small parameter size.
Whereas GPT-4 scored a whopping 67% in accuracy GPT-3.5 scored merely 48.1% in accuracy as ChatGPT-3.5 was trained on 175 billion parameters which is 1/10th of what ChatGPT-4 was trained on which was 1.76 trillion parameters.
Nonetheless, during GPT-3’s release, it had the largest neural network-based Artificial Intelligence model followed by Microsoft’s turning NLG model which had 10 billion parameters. The upside of GPT-3.5 is that it is accessible to individuals and businesses as it’s a cloud-based service that can scale according to the needs.
1. ChatGPT-3.5 has now become available to the public for free since GPT-4 has been implemented into the premium version.
2. ChatGPT-3.5 is way more cost-efficient as it takes $0.0015/1k tokens for input and $0.002/1k tokens for output compared to GPT-4’s $0.03/1k tokens for input and $0.06/1k tokens for output. this helps companies and users expand at a cheaper cost.
3. The availability of GPT-3.5 in ChatGPT has gained popularity in the Artificial intelligence generation space and has amassed around 100 million users within two months of its launch to the public. And the best part is it is free to use which boosted its user count over the year.
1. ChatGPT-3.5 does have some fair enough drawbacks since GPT3.5 has been trained on lower parameters compared to its successor GPT-4, it often shows inaccuracy when it comes to providing information.
2. ChatGPT-3.5 was established on pre-trained data before 2021 which causes it to not be up to date with the latest information regarding user queries which results in overall dissatisfaction of users as there are other AI chatbots out in the market for free which are up to date with the latest information like Bard from google.
3. ChatGPT-3.5 also has a flaw of not being able to understand user queries based on how they are worded since it has been trained on lower parameters it has caused it to not recognize certain queries that are worded differently.
4. The most major drawback of ChatGPT-3.5 is that it does not pose the ability to access the internet for more information which overall limits its ability to provide vast information.
GPT-3.5 can be used to its full potential for website creation tasks such as generative content, and optimizing SEO, for monetization, it can analyze user behavior, and create ad copy, And for marketing it can automate email campaigns and craft engaging social media posts.
3. PaLM 2 ( Bison-001 )
PaLM 2 ( Bison-001 ) is an LLM developed by Google AI which was released in May 2023. PaLM 2 serves LLM which powers Google’s AI chatbot Bard. it has been trained across various TPU 4 Pods and custom hardware designed specifically for machine learning which uses 340 billion parameters and is trained on 3.6 trillion tokens.
PaLM 2 is currently under development but it still can understand language, offer machine translation, code generation, generate natural language responses to questions, and many more things.
1. PaLM 2 is the predecessor of PaLM, which means it has been developed to perform better compared to PaLM, as PaLM 2 can be deployed on a vast range of applications.
2. PaLM 2 has proved to be more accurate compared to its predecessor PaLM as this helps it to be more reliable.
3. PaLM 2 is designed to be more secure compared to PaLM which helps it prevent itself from being used for malicious activities.
4. PaLM is capable of performing a lot of tasks which allows it to be helpful when it comes to getting a large amount of queries fulfilled at once.
1. PaLM 2 is very hard to train as it requires a large-scale database to be trained to execute queries which can surely be unwanted according to some businesses and individuals.
2. PaLM 2 does not go easy on the computing power requirements to deployed for use as it takes up a lot of computing power to execute queries which may lead to unwanted depletion of resources and incurring unwanted costs.
3. PaLM 2 has another major issue of being a complex tool to handle, this will cause individuals and businesses who do not have the right team trained to use PaLM will suffer when it comes to using PaLM 2 to its full potential.
PaLM 2 can be used to its full potential for website creation by eCommerce sites, personalizing user experience, and generating creative layouts, it can be used for monetization by data protection and privacy, selling data to protection and privacy solutions, and marketing the security of PaLM-powered websites, and It can also help in Marketing by creating case studies, and partnering with data protection and privacy organizations.
4. Claude v1
Claude v1 is an advanced LLM created by Anthropics which is supported by Google and was released on March 14th, 2023. Claude v1 was trained on 175 billion parameters. Its primary objective is to create AI systems that are safe and reliable.
Claude v1 uses an advanced architecture compared to other LLMs which makes it process information efficiently and allows it to make better predictions. Claude v1 is famous for its capabilities to allow anyone to understand, build, and grow a website without having prior knowledge of it.
1. Claude 1 can read, summarize, and analyze content from uploaded files which is by far the best feature that enables users to not waste time in typing out the data from files into prompts.
2. Claude 1 can process large amounts of words compared to any other AI chatbot which puts it above everyone as it allows 75,000 words per prompt and 100k words as output. This is all possible because of its LLM which uses advanced NLP to process and connect huge databases and find relations to generate larger outputs.
3. Claude 1 was trained with data up to the year 2022 which allows it to provide information regarding the after-pandemic world which is very important when it comes to doing research.
1. Claude 1 carries the biases of human data as it is yet to be trained on a larger more diverse scale to not give out biased output.
2. Claude 1 also had the issue of being hard to customize according to user preference as it needs Claude to be heavily trained on another set of data and highly modifying its core fundamentals which may be as good as making another AI tool at this point.
3. Claude 1 has a flaw of being not capable enough of performing all tasks equally which may lead to gaps in performance which in turn may lead to big issues such as misinformation and major errors.
Claude v1 can be used to its full potential for Website creation in which Automated management, SEO, and content creation and SEO are possible, and it can also help in Monetization via, custom engagement, and Ad customization, And it can also help in Marketing by refining landing pages, email marketing, and campaign optimization.
Cohere is an LLM that can be fine-tuned according to the enterprise’s specific use scenarios as it was released in June 2022. Cohere is trained on 52 billion parameters. Cohere’s company was founded by one of the authors of the research paper “Attention Is All You Need”.
Cohere has the advantage of not being restricted to one cloud platform compared to other LLMs like OpenAI. Cohere is known for its accuracy but it is more expensive compared to OpenAI models.
1. Cohere Ai can push boundaries as it allows users to automate various tasks, streamline processes, and enhance customer service.
2. Cohere can be fine-tuned as the ease of doing so is unmatched by other AI tools/chatbots till now.
3. Cohere is up to date with privacy and security concerns to avoid any mishaps in the future.
1. The only limitation Cohere has is that it has less brand awareness compared to other AI tools/chatbots which may lead to it not being profound among businesses which may in turn lead to future issues in terms of updates and stability.
Cohere can be used to its full potential for Streamlining content creation, subscription services, personalizing content, and much more.
Falcon is an open-source LLM that has three variants: Falcon 1B ( 1 billion parameters ), Falcon 7B ( 7 billion parameters ), and Falcon 40B ( 40 billion parameters ). Falcon was created by the Technology Innovation Institute on the transformer architecture which allows Falcon to be in a casual decoder-only model.
Falcon’s LLM was released on September 6th, 2023. Falcon comes under the Apache 2.0 license as it has been trained on higher-quality datasets. Falcon can be used to its full potential for Improving business communication, Tapping into Niche Markets, tailoring marketing, and much more.
1. Falcon can be multilingual as English is its main language now it can understand various other languages like German, and French.
2. Falcon can change the commercial usability in terms of being able to be fine-tuned according to user preference.
- Falcon does have the capability to be multilingual but falls short in terms of European languages.
LLaMA known as Large Language Model Meta AI released in February 2023, has two variants: A large one with 65 billion parameters and a small one with 13 billion parameters. The smaller variant is more capable and accurate compared to GPT-3. LLaMA’s primary focus is on educational applications and is mostly helpful for Edtech platforms.
While LLaMA can be used for tasks like including query resolution, reading comprehension, and natural language comprehension. LLaMA can be used to its full potential for improving interactivity, premium subscription-based content, and creating engaging content.
1. LLaMA can be more resource-efficient in terms of resource usage.
2. LLaMA does have a smaller parameter on which it runs which results in lower costs.
3. LLaMA is available for users as it is under a non-commercial license which widens the possibility and user diversity.
1. LLaMA is not capable as compared to other AI tools/chatbots which surely results in not the best output in terms of complexity.
Guanaco-65B as the name itself suggests has 65 billion parameters, it is an open-source model that is derived directly from LLaMA and fares well compared to other LLMs.
Guanaco competitor GPT-4 from OpenAI does not stand a chance since Guanaco’s text generations are faster cause of less computational resources required. Guanaco has various variants ranging from 7B, 13B, 33B, and 65B which is their largest version trained on 65 billion parameters.
1. Guanaco is an open-source model which allows it to be more fine-tuned according to user preferences.
2. Guanaco users have lesser computational power compared to the most popular AI chatbot/tool ChatGPT by OpenAI as it allows users to get the same results with lesser computational power.
1. Guanaco fails at doing Math which is its biggest letdown since it runs off a 4-bit interface which has limitations.
9. Vicuna 33B
Vicuna 33B is another open-source model derived from LLaMA that was released in April 2023. As the name suggests Vicuna 33B has been trained on 33 billion parameters.
Vicuna is well-tuned using data collected from sharegpt.com, a platform where users share their ChatGPT conversations. The Vicuna does not come at par with GPT-4 but it surely performs well based on the language and parameters it was trained on.
1. One of the biggest Advantages Vicuna has is that it can run locally and help patient privacy be maintained.
2. Vicuna is an open-source model that helps users with its scalability and has an advanced database to work with.
1. Vicuna has some limitations in terms of solving math or reasoning queries.
2. Vicuna also has a limitation in providing factually correct information.
3. Vicuna has yet not been polished enough for safety in terms of being potentially toxic and having biases.
MPT-30B is yet another open-source model based on LLaMA that was released on May 5th, 2023. It is trained on data sets from Camel-AI, GPTeacher, Baize, and ShareGPT, offering an astonishing context length of 8000 tokens.
MPT-30B directly outperforms GPT-3 from OpenAI. MPT-30B is trained on 30 billion parameters and it is so well-optimized and is smaller in scale compared to its competitors that you can even run it locally on your system.
1. MPT can handle longer input queries by users.
2. MPT is equipped with highly efficient open-source training code.
1. MPT does not pose the ability to be deployed without being fine-tuned.
2. MPT does produce factually incorrect outputs as it was trained on various public databases.
30B-Lazarus is developed by CalderaAI which uses LLaMA as its foundational model and was released in June 2023. The devs have used LoRA-tuned datasets from various models which include GPT-4, SuperHOTm Alpaca-LoRA, and many more.
As a direct result, the LLM performs better on various benchmarks. 30B-Lazarus falls short by a very small margin compared to Falcon and Guanaco. 30B-Lazarus is best for text generation as it lacks conversational chat responses.
WizardLM is an open-source LLM that is built to follow complex instructions and was released on May 26th, 2023. The LLaMA model is trained via a group of AI researchers who rewrite instructions into more complex instructions which they feed into WizardLM which is then used to LLaMA.
The surprising part is that WizardLM has just 13 billion parameters yet its output is far more satisfactory compared to OpenAI’s ChatGPT.
1.WizardLM can set simple instruction queries and turn them into more complex to turn them into higher quality instructions.
1. WizardLM’s drawback is that in development its database cannot be automated as it needs human interference to check the quality of the data to avoid biases in the future.
GPT4ALL is a project that is run by Nomic AI, GPT4ALL can run in-house models to your Local LLMs with ease on your computer without any dedicated GPU or internet connection. It has a compact 13 billion parameters model. GTP4ALL also has 12 open-source models from different organizations as they vary from 7B to 13B parameters.
The best part about GPT4ALL is the ease of installation and setup which has never been this easy for any LLM, all you have to do is get the GUI installer select the model you want to work with, and install with a click of a button you have access to your very own LLM.
As days go by LLM keeps advancing and finding new ways to get integrated into your business model. We already have a variety of LLMs to choose from to help us grow our corporations. Having access to the best LLM at your disposal is crucial to ensure effective progress.
The Best LLM for your work will depend on your budget and your needs. If you ever get confused about getting one LLM why not give both a try alternatively to see which suits your needs better? The best would be to know more about LLM and get ahead of everyone in understanding its true value while integrating it and getting hands-on experience with it.