The evolution of the language process: enhancing AI with LLMs | Almawave
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Artificial Intelligence

25 October 2023

The evolution of the language process: enhancing AI with LLMs

One of the biggest changes in AI is undoubtedly generative AI.

Generative AI systems focus on creating original content based on specific requirements and requests from human users, which are called ‘prompts’. Generative AI systems show amazing capabilities in generating music, images, videos, code and, most importantly, text that is often indistinguishable from human-generated content.

Text generation is one of the most intriguing aspects of generative AI, and this great achievement is the result of more than 50 years of research in the field of Natural Language Processing (NLP).

Systems such as OpenAI chat GPT, Google Bard and others are some of the most famous generative AI systems using Large Language Models (LLM).

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What are Large Language Models and how do they work?

LLMs have been specifically created to handle and analyse large amounts of natural language data. Using this data, they generate answers to user queries (prompts). The training process involves exposing these systems to huge datasets and using advanced deep learning algorithms to understand the complexities of human language. As a result, they have the unique ability to produce natural responses for a wide range of written inputs.

The considerable capabilities of Language Models in generative AI are based on enormous amounts of data. To efficiently manage and process this data, as well as handle the computational demands of building such models, GPU-equipped machines are indispensable.

Over time, the growth in computing power, particularly through the availability of powerful resources such as Graphics Processing Units (GPUs), coupled with advances in data processing techniques, has enabled researchers to train significantly larger models.
These last two considerations are some of the main reasons why generative AI has only recently become relevant. Although the evolution of these systems has already been rapid, the release of generative AI to the general public has accelerated this process even more.

The capabilities of generative AI applied to language have created great expectations, stimulating the AI industry to increasingly exploit this technology. The applications of generative AI in language offer numerous advantages in various NLP fields. For instance, Large Language Models excel in content generation, summarisation, paraphrasing and language translation.

They can create outlines and initial drafts from detailed prompts, serving as invaluable brainstorming tools. LLMs can also efficiently summarise and paraphrase entire telephone conversations or client meetings, condensing huge amounts of text into essential information for easy comprehension. Language translation becomes easy with LLMs, facilitating the globalisation of content. They play a crucial role in virtual assistants, offering support in customer interactions, troubleshooting and open conversations with users. They also contribute to code generation and correction, providing useful code snippets based on natural language requests.

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Challenges and benefits of generative AI

Generative AI offers advantages in the language process, but also presents challenges in model governance, output explicability and fine-tuning. The main disadvantage is the enormous amount of computational resources required for training and providing answers. Consequently, these Large Models are bound to remain under the control of organisations, raising concerns about privacy, security and reliance on ‘black box’ models. Due to the computational demands, these services are not offered for free beyond basic use, making cost a significant factor for large-scale implementation.

In the world of artificial intelligence (AI), particularly with the emergence of Large Language Models and Generative Artificial Intelligence, there is a growing concern about the quality and reliability of AI outputs. The saying ‘garbage in, garbage out’ emphasises that if these models are trained on biased or erroneous data, their outputs will also be biased and unreliable.

A significant challenge in this landscape is access to data, which often contains sensitive information and resides in secure environments limiting the use of LLM and other AI techniques.

Almawave’s philosophy embraces a concept called ‘composite AI’. In this approach, we have developed ‘ensemble’ models that intelligently combine the power of next-generation Large Language Models with Almawave’s proprietary techniques. These techniques include extractive summarisation, vector search, data anonymisation and natural language structured data access algorithms.

Using ensemble models, Almawave can guarantee controlled and reliable use of LLM on private and/or certified data within a business context. This addresses data privacy and security concerns while harnessing the capabilities of powerful AI models.

The application of these ensemble models opens up a realm of possibilities for various use cases within enterprises. For example, in conversational AI, models can interact with users in natural language, offering valuable information and support. In information discovery, ensemble models can efficiently scroll through both structured and unstructured data, enabling quick and relevant insights. Furthermore, in speech analysis, they can process spoken language to extract valuable data and patterns.

By combining the strengths of LLM and the techniques of Almawave, businesses can confidently harness the potential of AI in domains that require reliable and controlled access to data. This improves productivity and efficiency, while ensuring compliance with data privacy regulations and ethical considerations.