Artificial Intelligence (AI) has undeniably revolutionised the technological landscape of the 21st century, driving innovative advancements across a multitude of industries. One such noteworthy development in AI is the advent of large language models (LLM), which have rapidly become an integral component of our digital interactions.

But what exactly is a large language model? How do these advanced models operate, and what does it entail to train and fine-tune them to function effectively and responsibly?

Unpacking the LLM concept: The powerhouse behind simulated human-like text

In essence, a large language model is a sophisticated AI tool that employs deep learning techniques to generate text that mimics human-written content. This ability to produce coherent, contextually relevant, and useful text is achieved by training the model on vast volumes of data. By predicting the next word in a sentence based on the previous ones, LLMs can construct meaningful sentences and paragraphs.

Take, for instance, virtual assistants such as those found on smartphones or smart home devices. These tools interact with users by interpreting and responding to human language in a helpful and contextually suitable manner. Such functionality is driven by an LLM that’s been trained to process and respond to human language effectively.

However, while LLMs can generate impressively human-like text, it’s crucial to clarify that they don’t possess human-like comprehension. LLMs lack beliefs, desires, or consciousness. They do not form opinions or have personal experiences. Their responses are purely algorithmic, generated based on patterns identified during training, devoid of genuine understanding or intent.

Pre-training: The cornerstone of LLM

The journey of creating an efficient LLM is a multi-faceted process that primarily involves two key steps. The first of these is pre-training, during which the LLM is exposed to a broad dataset containing parts of the internet. However, the LLM does not retain specifics about which documents were in its training set, nor does it have access to any particular documents or sources.

In pre-training, the LLM learns the fundamental structure of the language. It begins to recognise syntax, grammar, common phrases, and even incorporates general knowledge or facts about the world. However, it’s important to note that these are general patterns in the language, and the LLM does not possess a true understanding of these facts in the same way as a human does.

Consider the pre-training phase as analogue to a child learning a language. Initially, they listen to the conversations around them and begin identifying patterns in the language—repeated phrases and structures. Pre-training an LLM is somewhat similar, but the model doesn’t comprehend the meaning behind these words like a child eventually does.

Fine-tuning: Refining the machine’s output

After the foundational pre-training phase, the second pivotal step is fine-tuning. Fine-tuning is a narrower and more specific phase of the process that significantly contributes to the effectiveness of a large language model. It allows an LLM to adapt its general language understanding acquired in pre-training to more specific tasks or nuanced interactions.

This critical process could be likened to a child now learning how to use language in specific social contexts, understanding when certain phrases are appropriate, and learning nuances to apply in their interactions.

Understanding fine-tuning: The shift from general to specific

After the completion of the foundational pre-training phase, the focus shifts towards fine-tuning. It is during this phase that the general knowledge gathered in pre-training is honed to handle more nuanced tasks or domains.

For example, Identrics’ award-winning Multilingual Abstractive Summarisation technology enables the automated generation of near-human-like abstracts of lengthy texts from various languages, ensuring accurate and concise English summaries. By leveraging a combination of machine learning models, the algorithm processes the text and produces a shorter retelling in its own words.

Fine-tuning a pretrained model, such as Identrics’ Abstractive Summary solution, involves using a smaller but more specific dataset to train the model further. This more detailed and specific data empowers the solution to deliver precise and pertinent abstractive summaries tailored to the specific task at hand.

Executing fine-tuning on LLMs

Executing the fine-tuning process of a large language model is a delicate procedure that requires oversight and collaboration between the model’s developers and human reviewers. The reviewers follow a set of guidelines, reviewing and rating potential outputs from the model across a range of inputs. Their feedback is then used to inform the model’s future responses.

Specifically, the process involves:

  • Data set preparation

To begin fine-tuning a large language model, the initial step is to clearly define the target task and assemble an inclusive dataset. A prime example of this is our Abstractive Text Summarisation model, where we carefully curated a dataset of selected news data together with an abstract for each article. This meticulous dataset preparation ensures that the model accurately represents the information and avoids biases.

  • Fine-tuning

The model undergoes a fine-tuning phase using the prepared data set, where the ratings and feedback from reviewers are utilised to improve its performance.

  • Output review

Once fine-tuning is complete, the model’s responses are reviewed to assess their quality, context suitability, and adherence to guidelines.

  • Feedback loop

The ratings, comments, and insights obtained from the output review are incorporated into the iterative process, guiding further improvements and refining the model’s behaviour over time.

The iterative nature of fine-tuning language models

Fine-tuning language models is not a one-off task but an iterative process. Reviewers meet regularly with the developing team to discuss any challenges, questions, or insights from their reviewing process. This feedback is then used to continually refine the model’s responses, ensuring its ongoing improvement and alignment with intended behaviour.

Moreover, fine-tuning is a critical safety measure. By allowing developers to guide the model’s behaviour, they can ensure that it aligns with ethical guidelines and avoids generating potentially harmful or inappropriate content.

This iterative fine-tuning process, coupled with the feedback loop with human reviewers, is essential in ensuring that the large language model remains a responsible and effective tool in AI applications. These measures allow for the utilisation of AI’s vast potential, enabling the model to evolve and improve continuously over time.

The broader role and implications of LLMs

LLMs have significantly influenced the AI landscape, introducing a new era of technological capabilities. The utility of these models extends far beyond simple text generation. They’ve found applications in diverse areas such as customer service, content creation, healthcare, and many more.

However, the development and fine-tuning of LLMs also underscore a critical need for careful management to ensure the model’s outputs remain free from hallucinations, unfaithful content, and/or hate speech.

As AI technology continues to evolve, the importance of safe and responsible AI cannot be overstated. Through continuous improvements in training and fine-tuning methodologies, we strive towards a future where AI not only enhances technological capabilities, but also prioritises the welfare and safety of its users.

About the author

Dr. Yolina Petrova

Senior Data Scientist / Machine Learning Engineer

Dr. Yolina Petrova is a seasoned professional who seamlessly blends the realms of Cultural Studies and Cognitive Science. As a Senior Data Scientist at Identrics, she brings her expertise to the forefront of developing cutting-edge AI solutions. Alongside her industry role, Dr. Petrova also imparts her knowledge and passion as a lecturer in Statistics and various cognitive courses at NBU.

Driven by a profound curiosity, Dr. Petrova’s research interests revolve around unraveling the intricate dynamics of trust between humans and artificially intelligent systems. She delves into exploring the ways in which this trust can be nurtured and facilitated to foster the production of socially beneficial and responsible AI. Additionally, her pursuits encompass the captivating field of Cognitive Modeling, where she focuses on modeling cognitive abilities that have long been considered distinctively human.

With her unique blend of academic prowess and practical experience, Dr. Petrova continues to make remarkable contributions to the advancement of AI and its ethical implications. Her work exemplifies a deep commitment to bridging the gap between human and machine, all in the pursuit of creating a brighter and more harmonious future.