In recent years, there has been a rapid development of LLMs. New models are constantly being released, each with its own strengths and weaknesses. This can make it difficult for developers and businesses to choose the right model for their application.
From Unique’s experience, we know that finding the right solution for a specific industry like Banking or Insurance can be quite challenging. But based on what we've learnt, we can already see some of the advantages of certain LLMs and approaches that we are going to explain further in this blog post.
Large language models (LLMs) are a type of artificial intelligence that are trained on massive datasets of text and code. They can be used for a variety of tasks, such as generating text, translating languages, and writing different kinds of creative content.
This blog post will compare some of the most popular LLMs available today. We will discuss their strengths and weaknesses, and we will provide some recommendations for how to make up your mind based on our experience.
As mentioned before, the development of AI is an ongoing process. There are a lot of LMs that are currently under development and will be released soon. However, it’s easy to distinguish the front-runners of this category already:
All of the models mentioned above, except for GPT-3.5 and GPT-4, are open source, meaning that they can be freely used and modified by anyone. This makes them a valuable resource for developers and researchers who want to use large language models in their applications.
In terms of performance, the models vary depending on the task at hand. For example, GPT-3.5 and GPT-4 are generally better at generating text than Llama or Falcon. However, Llama and Falcon are more efficient and require less computing power to run. Ultimately, the best model for a particular task will depend on the specific requirements of the application.
To illustrate the LLMs capabilities, take a look at this FLASK evaluation framework (Fine-grained Language Model Evaluation based on Alignment SKill Sets) that mentioned a few of the abovementioned language models.
Based on this chart, you can clearly see that, at the moment, GPT-4 outperforms all other language models in all categories except for Harmlessness, where GPT-3.5 and Claude lead the way.
In terms of performance, GPT-4 is generally the best performer across all tasks. However, it is also the largest and most computationally expensive model. Bard is also a very capable model, but it is still under development. Llama and Falcon are both smaller and more efficient models than GPT-4 and Bard, but they may not be as accurate or versatile.
Ultimately, the best model for a particular task will depend on the specific requirements of the application. If you need the best possible performance, then GPT-4 is the best choice. If you need a more cost-efficient but still very capable model, then Bard, Llama, or Falcon may be a better option.
At Unique, we currently use a multitude of AI solutions. Among them you can find GPT-4, GPT-3.5, GPT-3.5 Turbo, GPT-3.5 Turbo 16K, Whisper, Microsoft transcription service, and BERT, as well as LlaMA 2.0 and Falcon.
These models serve different purposes: analyze the content uploaded to our platform, extract insights, create transcriptions for call recordings, extract topics, generate custom content based on request, etc.
As we have already gained some valuable experience with our clients like Pictet and LGT, we have a vast knowledge of enterprise-specific issues when it comes to implementing an AI solution.
As a general approach, we advise our clients against training their own models. Feeding sensitive company information to further develop AI’s capabilities can be risky. Instead, we offer RAG (Retrieval-Augmented Generation), the concept, which involves using pre-trained models like GPT-3.5 or Llama to generate text based on prompts and existing sources uploaded to the platform. However, it's important to emphasize the need for configuring RAG specifically for each industry and use case.
Another important issue is maintaining document privacy and access control. It’s imperative to have an enterprise-ready retrieval model that can synchronize with existing access levels. This can help avoid security and compliance issues in the future, as only authorized users can access sensitive information uploaded to the platform.
Our Unique FinanceGPT system is model agnostic, meaning that we can use different models like GPT-3, GPT-4, or Llama depending on the use case and desired results. This means that our clients don’t have to choose a specific language model that will be responsible for generating a response to their request. Based on prompts and configurations, Unique platform chooses the best solution to provide the maximum efficiency depending on the use case.
At Unique, we employ a compliance layer that combines several elements of IT security, data protection and legal frameworks. We aim to make it easy for the user to follow GDPR-principles by employing built-in privacy by design and privacy by default mechanism in all our processes (e.g. automatic watermarking of AI-generated content). We control all information that is sent and received by Large Language Models (like GPT models offered by Microsoft) and make sure to filter out all personal identifiable data by means of pseudonymization. In addition, we have several opt-outs in place to make sure no data is stored by OpenAI and the prompts are not checked for harmful content. Therefore, our users are strongly recommended to follow responsible prompting guidelines.
The rapid evolution of Large Language Models (LLMs) has ushered in a new era of AI capabilities, with each model boasting its own set of strengths and potential applications. Unique's journey through this landscape has been marked by hands-on experience with various models, from GPT-4's unparalleled text generation prowess to the efficiency of Llama and Falcon. Our diverse AI toolkit, which includes models like GPT-4, GPT-3.5, and LlaMA 2.0, enables us to cater to a wide range of industry-specific needs, ensuring optimal results for our clients.
As the LLM domain continues to expand, Unique remains committed to exploring new models, refining our techniques, and delivering top-notch AI solutions to our clients.