The pace of technology evolution is blinding and never more-so than in the AI space. In 2023 we have seen the emergence of a veritable AI arms race. You’d have to be living under a rock to have missed the release of Open AI’s ChatGPT last year which has been closely followed by a hoard of capable (and not so capable) models. It can be hard to keep up with modern developments but in this guide to large language models (LLMs) in 2023 we are here to help!
We will explore the current LLM landscape touching briefly on the major open and closed source models. We’ll take a brief look at the current closed and open-source models to understand their real-world applications, performance metrics, and some use considerations. So let’s get into it.
What Even is a LLM?
Large Language Models are AI models that leverage the power of machine learning algorithms to comprehend, generate, and predict text in a manner that mimics human language abilities. Built upon the principles of Natural Language Processing (NLP), these models work by training on an extensive corpus of text data, learning linguistic patterns, syntax, and semantics. This allows them to generate coherent and contextually relevant text based on the input they receive.
As they process an input, they analyze the structure and context of the text, making predictions about what comes next. This is not just limited to word prediction but extends to entire sentences and even paragraphs, giving them the ability to write essays, write code, answer questions, and much more.
The TL:DR; version is that this are basically just very, very good next word (really, next character predictors). The power of these models lies in their ability to handle tasks requiring a deep understanding of language and context. This includes tasks like sentiment analysis, language translation, text summarization, and question answering.
With the ongoing advancements in this field, LLMs continue to refine their ability to understand subtleties in language. This includes handling ambiguity, and generating creative responses, bringing us ever closer to the goal of creating truly intelligent, artificial general intelligence (AGI).
Understanding Open Source vs. Closed Source LLMs
Open source and closed source models represent two distinct approaches to the development and distribution of Large Language Models.
Open source models, as the name suggests, are publicly accessible and can be freely used, modified, and distributed by anyone. This openness encourages a collaborative development approach. Here a diverse community of developers can contribute towards refining and improving the model. The transparency of open source also allows for extensive peer review, which can lead to more robust and reliable models. According to Huggingface, top open source models currently include Meta’s Llama 2, Stability AI’s Stable Beluga, Airoboros, and Falcon.
On the other hand, closed source models are proprietary and their underlying code (or training modalities and data) is not publicly available. These models are typically developed, owned, and maintained by specific organizations or companies, and they retain full control over how these models are used and distributed.
Closed source models can provide competitive advantages for their owners and often come with dedicated support and frequent updates. However, their secretive nature can make it difficult for outside researchers to understand their inner workings or verify their reliability and fairness. The biggest open source models currently out include OpenAI’s GPT-4, Anthropic’s Claude 2, Microsoft’s BingChat, and Google’s Bard .
Open Source Options
Closed source LLMs provide a range of benefits that can be appealing in their application. One of the key advantages is the high level of control it offers to the creators. They can decide who can access and use the model, thus protecting their intellectual property. In competitive industries, this can be particularly advantageous, helping organizations maintain a competitive edge.
Closed source LLMs also often come with great support from the open source community. This means that users can rely on a team of professionals and enthusiasts for assistance, problem-solving and regular updates, ensuring that the models stay current and efficient.
Lastly, closed source models can provide more security. Because their inner workings are not publicly available, they can be less vulnerable to malicious tampering or misuse. Additionally, users can control data in their own fashion. This can make them more suitable for sensitive applications where security and privacy are paramount.
Closed Source Considerations
Closed-source Large Language Models, such as GPT-4, Claude 2, BingChat, and Bard, have numerous benefits. Firstly, they promise high-quality and consistent outputs. They’re trained on expansive, carefully chosen datasets, usually encompassing a diverse data range and thorough fine-tuning. This results in powerful models that give accurate results, a crucial trait for businesses relying on dependability. Additionally, these models’ makers are dedicated to enhancing their performance, regularly updating them to boost functionality and rectify any issues.
Another significant advantage of closed-source LLMs is their robust support and security framework. Unlike many open-source alternatives, these models come with dedicated support teams. Users can count on swift replies to their questions and steady support during the model’s use. In terms of security, closed-source LLMs stand strong. They retain more control over their models’ use, preventing misuse effectively. This makes them an appealing choice for organizations that prioritize data security and confidentiality. Furthermore, these models often have protocols in place to hinder unethical or damaging use, aligning them better with responsible AI practices.
How to choose?
Choosing between open and closed source LLMs is not a straightforward task. Several factors come into play, like budget, need for customization, and control over the learning model.
Open-source LLMs usually suit projects with limited funds and a high customization demand. Their transparency and collaborative nature may also be a plus for teams eager to learn from and contribute to the broader developer community.
In contrast, closed-source models often bring professional support and regular updates to the table. This can benefit teams seeking reliability and well-documented modifications. These models may also boast unique features or algorithms that offer a competitive edge in certain uses. However, they often come at a higher cost, and customization options might be limited.
The level of technical expertise is another important factor. Teams with strong technical skills might favor open-source models, given they can maximize these models’ customization potential and handle issues autonomously. On the other hand, teams with fewer technical skills may lean towards closed-source models, which typically provide dedicated support and maintenance services. Therefore, assessing your team’s technical capabilities and resources is crucial when selecting the most fitting LLM model.
Choosing between open and closed source LLMs is a matter of matching the project’s specific needs and resources. It’s a decision requiring a careful weigh-in of both model types’ pros and cons.
So what can they really do?
The use case ideas for LLMs in modern applications is practically endless. With increased ability to integrate with a multitude of APIs it is up to the developers imagination. That said, some of the applications we’ve seen so far this year include:
- Automated Content Generation: LLMs can be used to create automated content for websites, blogs, and social media platforms, reducing the burden on human writers and accelerating content production (JasperAI, ChatGPT).
- Sentiment Analysis: By analyzing text data, LLMs can determine the sentiment behind customer reviews or social media posts, assisting businesses in identifying trends and improving their services.
- Language Translation: LLMs can be used to develop sophisticated translation services, breaking down language barriers and facilitating global communication.
- Educational Tools: LLMs can create personalized learning materials, adapt to individual learning pace, and even answer student queries, revolutionizing the education sector (ConsensusAI, QuillBot).
- Chatbots: LLMs are instrumental in creating chatbots for customer service, providing instantaneous, accurate responses, and improving customer experience (Replika).
- Data Mining and Text Extraction: LLMs can be used to mine and extract relevant information from large data sets, saving time and resources. This is becoming particularly useful in the world of API and webscraping as the link between NLP and code continues to become more functional.
All-up
In the whirlwind of the AI evolution, Large Language Models (LLMs) have emerged as crucial game-changers. These AI marvels are transforming everything from content creation to customer service and education.
In this easy guide to LMs we can see they come in different flavors: the community-driven open source models like Llama 2 or Stable Beluga, which foster innovation through transparency, and the proprietary, closed-source models like Claude 2 or BingChat, known for their robustness, support, and control over usage.
Each kind of model has its strengths and caters to different needs. But irrespective of their source, these AI models are an integral part of our digital lives. The key to navigating this rapidly evolving landscape is understanding your specific needs, resources, and goals.
In this thrilling era of AI, LLMs are not just tools but trailblazers, guiding us into a future where human and machine communication intertwine seamlessly. So, let’s embrace this future with excitement, responsibility, and respect for the transformative power these models hold. The AI future isn’t a distant dream; it’s here, now, and it’s ours to shape.
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