Future of Large Language Models: Speculating the advancements, improvements, and transformations in LLM technology

The fairly recent launch of ChatGPT has brought about a whirlwind interest in the concept of AI, NLP, and LLMs. Of course, Artificial Intelligence, the bigger umbrella is the one that is most often talked about in this regard, and the massive demand and predicted growth of the AI market demonstrates that the technology is here to stay for a long time to come. The global AI market size is projected to reach $1,811.8 billion by 2030 (Source). NLP a branch of AI is also witnessing a massive interest as the global NLP market is expected to go from $3 billion in 2017 to $43 billion in 2025 (Source). As AI is becoming a household name, more and more people are looking for AI-driven no-code platforms that will help them leverage this cutting-edge technology to boost their business growth without investing hundreds of thousands of productive man-hours.

What is a Large Language Model?

A large language model is a type of AI model that is designed with the intent to generate and understand human-like text by analyzing vast amounts of data. Based on deep learning techniques, these models typically include neural networks with multiple layers and a large number of parameters, letting them capture complex patterns in the data they are trained on. The idea is to understand the structure, syntax, semantics, and context of natural languages so that they can generate coherent and contextually correct responses to queries or complete the text inputs with relevant information. Though there is still a lot of work being done in these fields and it seems to only be at nascent stages, there are some clear potential innovations in LLM technology that seem to be on the precipice of bringing about a massive change. To ensure this capability, LLMs need to be pre-trained on massive amounts of data through fine-tuning, in-context learning, and specializing.

Best Large Language Models

Though there are a number of Large Language Models, many people have only ever heard of GPT 3. Let’s list out all the popular large language models.

1. T5
T5 or Text-to-Text Transfer Transformer is a pre-trained LLM leveraging a transformer architecture to carry out several tasks related to natural language processing. T5 is different from other models in the fact that it can perform multiple tasks using a single model with a text-to-text transfer approach. This allows the model to adapt to different tasks with only a little fine-tuning. The model has about 11 billion parameters.
2. GPT 3 & GPT 4
GPT-3, or Generative Pre-trained Transformer 3 from OpenAI has recently garnered a lot of attention for being exceptionally adept at understanding and generating natural language. GPT-3 is the third iteration in the GPT series and became available for public use when it was developed into GPT-3.5 for the creation of ChatGPT. GPT 3 uses 175 billion parameters making it way better than most of the other models in the market.
3. LaMDA
Just like the other names on the list, LaMDA can learn text representations that can then be used for a variety of NLP tasks. But there are a number of other ways in which LaMDA is entirely different from anything else. Two such differences that clearly stand out are, one, that the platform has 1.6 trillion parameters and two, that it uses a new architecture called Switch Transformer. It is because of this unique architecture that the model can easily switch between various task-specific modules whenever the need arises.
4. BERT
BERT or Bidirectional Encoder Representations from Transformers, is based on the Transformer Neural Network architecture from Google. BERT was the first one to validate and carry out the switch from NLP to RNNs. BERT is trained bidirectionally, which means it has a more evolved understanding of the context and flow of languages when compared to the unidirectional models.
5. RoBERTa
RoBERTa is yet another pre-trained LLM based on the BERT architecture but is fine-tuned with a whole lot more extensive and diverse data sets. RoBERTa delivers a state-of-the-art performance on numerous NLP tasks, such as text classification, question answering, and language modeling, and has 355 million parameters.

Future Trends in Large Language Models

Though it is tough or maybe impossible to predict the future, there is a lot of research being done on LLMs mainly aimed at straightening out the kinks that we still encounter while using these models. Let’s take a look at the three significant changes that researchers are working on.

1. Self fact-checking

The first change that we can expect is to work on improving the factual accuracy of LLMs by enabling them to fact-check themselves. Doing this will let the models access external resources and offer citations and sources for their responses making it better suited for real-world applications. Two models, introduced in 2020, Google’s REALM and Facebook’s RAG feature significant research in this field. And if move towards more recent developments, WebGPT from OpenAI makes great use of Microsoft Bing to browse the internet and generate more accurate and comprehensive responses. WebGPT mimics humans as it submits a search query to Bing, clicks links, browses web pages, and even deploys functions like CTRL+F to locate relevant information. To make it even more reliable, the model also includes citations, letting people authenticate the source. In fact, WebGPT outperforms all the GPT 3 models in terms of accuracy percentage and the amount of truthful and informative responses provided. These models represent only the initial phase of exploring this future trend. True, it is too soon to say with conviction whether any of these upcoming models will be able to address and resolve the problem of accuracy, fact-checking, and a static knowledge base, but the future does seem to be promising.

2. Need for better prompt engineers

Though LLMs have made leaps of progress and will continue to do in the future as well, they are still far behind humans when it comes to a complete understanding of languages. In fact, this lack of understanding may cause goof-ups that will be tough to swallow for people using the models for text generation. In order to address this issue prompt engineering techniques have been developed. Prompt engineers can help the model come up with more relevant and accurate responses to even the most complex queries. Two of the most popular examples of prompt engineering techniques are Few Shots learning and Chain of Thought prompting. In Few Shot learning, you create prompts with a few similar examples and the desired outcome which serve as guides for the model to generate responses. Chain of Thought prompting is a series of techniques best suited for tasks that need logical reasoning or step-by-step computation.

3. Improved Approaches for Fine-Tuning & Alignment

Customizing an LLM is an absolute essential and fine-tuning them with industry-specific datasets can significantly improve their performance. Doing this is of particular importance when you are using the LLM for highly specialized domains. Apart from the traditional fine-tuning techniques, there are new approaches emerging to further enhance the accuracy of LLMs. For example, Reinforcement Learning from Human Feedback (RLHF), which was used to train ChatGPT. Using RLHF, users can provide feedback on the LLM responses. These feedbacks are then used to train a reward system to fine-tune the model and align it better with user intents. This is the primary reason why ChatGPT4 performs better than the previous models when it comes to following instructions. An entire new generation of LLMs is on the way to a meteoric rise and we are witnessing its evolution from its predecessors to something that will truly amaze even the seasoned AI experts.

Conclusion

NLP and Large Language Models have truly taken the landscape by storm and have made significant strides in the field of artificial intelligence. New concepts like applying quantum computing in large language models are emerging on the spectrum of innovations in LLM. However, as the technology keeps evolving it is exciting to wait and watch how future developments will address the remaining challenges that LLMs face. Though there have been big strides in terms of fact-checking, fine-tuning, and prompting techniques, there is a lot that remains to be done.

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