Over the last few months, GenAI has become a ubiquitous topic, permeating all sectors, and significantly enhancing our interaction with technology. It has introduced a new way to bring intelligence to every sphere, opening many possibilities for organisations in automation, knowledge management, research, software development, customer servicing, and more. 

According to a report by Nasscom and McKinsey & Company, GenAI is now expected to generate economic value worth a whopping $2.6-4.4 trillion annually. At the heart of this revolution is large language models (LLMs), which possess unparalleled natural language processing capabilities. The development of these LLMs involves extensive R&D, experimentation, and persistent efforts. To fully leverage their benefits, businesses must grasp the intricate structure and attributes.

Language models (LMs) are mathematical constructs crafted to understand, generate, and manipulate human language. These models excel in diverse applications, spanning machine translation, voice recognition, text summarisation, and chatbots. Language models predict the likelihood of a sequence of words appearing in a text. This is achieved by learning the probabilities of different words and phrase sequences, enabling them to statistically generate text akin to their training data.

This article briefly delves into the shortcomings of early language models and how LLMs like ChatGPT came into being.

Early language models, which served various purposes in the realm of natural language processing like machine translation, speech recognition, information revival, spell check/correction and text generation, had their limitations. The advent of transformers technology addressed these limitations and brought forth several advantages.

Firstly, they can do things faster because they look at all the words at the same time. Secondly, they’re great at understanding long sentences because each word can talk to any other word directly. Thirdly, they’re smart in choosing what is important in a sentence. Lastly, they work better as they get bigger and have more data to learn from.

However, transformers also come with their own set of drawbacks. They demand substantial computational resources and a vast dataset for effective learning. Consequently, this prompted the creation of LLMs.

LLMs use a massive amount of text and numerous rules to become exceptionally proficient at understanding and using language. Imagine a gigantic library filled with billions of books — that’s how much information they possess. LLMs leverage extensive text data and millions, even billions, of parameters, capitalising on the principle that “scale and complexity breed emergence.” Here, scale encompasses both model size and training data volume.

As LLMs grow in size and train on more data, they manifest “emergence” — displaying unanticipated behaviors and capabilities. Larger models, with surplus of parameters, allow them to learn various patterns from the data, highlighting the transformative potential of scale and complexity in language modeling. Furthermore, emergence in LLMs, such as zero-shot learning and in-context adaptation, demonstrates their ability to perform untrained tasks.

Limitations and challenges

While emergence can yield impressive capabilities, it can also introduce challenges, including unpredictability and potential undesired behaviors in models. LLMs, while powerful and versatile, present difficulties in foreseeing actions in specific scenarios, occasionally producing incorrect or inappropriate results. Managing and comprehending LLM behavior is an ongoing research focus.

Additionally, LLMs are bound by potential biases and errors in their training data, making control and interpretation of very large models challenging, particularly in contexts of transparency and accountability. Hence, organisations and institutions utilising LLMs must thoroughly understand their behavior and limitations before investment.

The writer is Managing Director – AI and Data Science, Nagarro