7 Large Language Model (LLM) Trends To Watch In 2024

The buzz around large language models (LLMs) has skyrocketed, especially since the debut of ChatGPT back in November 2022. These LLMs have been real game-changers, revolutionizing multiple industries by generating human-like text and tackling a vast array of tasks. However, there’s the other side of the coin. There are concerns about the biases and inaccuracies that limit their worldwide adoption and raise some ethical concerns.

This article isn't just here to point out problems; it's all about solutions. We're diving deep into the future of these models, exploring promising strategies like self-training, fact-checking and tapping into sparse expertise. These approaches hold the key to mitigating these issues and unleashing the full potential of these models. Buckle up. It’s going to be an adventurous ride.
 

Trend 1: Self-validating LLMs Will Be A Game-changer

People often say that ChatGPT and other LLMs are about to take the place of Google Search as the most popular way to find information. But these language models actually have a big problem: they make stuff up all the time, proudly giving out wrong or confusing information. These models can only use the data they were taught, so they can't talk about recent events or get the most up-to-date information. People are hesitant to count on facts that aren't reliable.

That being said, there is hope ahead. Models like WebGPT and Sparrow are looking into ways to get data from outside sources and give links, which would let them access and use correct, up-to-date data. Even though it's not a perfect answer, this method might help language models with AI hallucinating, making it possible for them to be used in the real world. It looks like AI tools might help us find information in the future but not yet. By the way, do you trust the data that AI tools give you?
 

Trend 2: Now, LLMs Will Improve Themselves

Humans have a remarkable ability to learn not just from external sources but also through internal reflection and analysis. Interestingly, a new frontier of AI research aims to empower LLMs with a similar capability – bootstrapping their intelligence by generating new content and using it to enhance their training. Initial studies have shown promising results.

For instance, Google’s latest LLM models can autonomously generate questions, deliver comprehensive answers, evaluate the quality of those responses, and fine-tune themselves based on this feedback. This process results in marked performance improvements, paving the way for more advanced AI models capable of independent learning and self-improvement. In a different method, LLMs create their own natural language directions to help them fine-tune themselves, which makes the base model much better. At first, this idea may seem like it goes in circles but LLMs can get better over time, just like people. We all learn from our mistakes, right?

 

Trend 3: It Will Be Crucial To Learn Prompt Engineering

Prompt engineering has indeed emerged as a crucial technique to enhance the performance and accuracy of LLMs, which still lack the complete understanding of languages that humans possess. This deficiency can lead to significant goof-ups, making prompt engineering an invaluable tool for mitigating such issues and guiding LLMs to generate more relevant and accurate responses, even for complex queries.

Among the most popular prompt engineering techniques are Few-shot learning and chain-of-thought prompting. Few-shot learning involves creating prompts with a few similar examples and the desired outcome, serving as guides for the model to generate responses. On the other hand, chain-of-thought prompting is a series of techniques particularly well-suited for tasks that require logical reasoning or step-by-step computation. What are your go-to prompts to create content?
 

Trend 4: Sparse Expert Models Will Go Beyond The Ordinary LLMs

Today's leading language models share a common architecture but an intriguingly different approach called sparse expert models is gaining traction. Unlike dense models that activate all parameters for every input, sparse models selectively activate only the most relevant "expert" parameters based on the input prompt. Recent research highlights the immense potential of sparse expert models.

For example, Google's GLaM architecture outperforms GPT-3 while using two-thirds less energy and requiring 50% less computing power, making it highly efficient. Sparse expert models also provide greater interpretability, with outputs coming from distinct "expert" parameter subsets, which is crucial for high-stakes applications like healthcare or finance. While technically more complex, the computational efficiency and interpretability advantages of sparse expert models could drive their wider adoption in the future of language models. Seems like an interesting concept, isn’t it?

 

Trend 5: You Will Be Able To Upgrade Yourself With LLM Plugins & Agents

With the major challenges of LLM training addressed, a new focus has emerged – integrating LLMs into real-world products. Innovations like plugins and agents enrich LLMs with additional capabilities, such as reasoning and leveraging non-linguistic data. While plugins allow accessing external data sources, agents, on the other hand, bring language and actionability together.

Frameworks like LangChain, AutoGPT and LlamaIndex make it easy for developers to integrate plugins and agents. These improvements make app prototyping faster but they also need organized methods for choosing the LLM, making sure it works with how the agents should behave. As LLM models get better, they make it possible for cognitive modeling to be more complete and for it to be used in the real world.
 

Trend 6: Your Feedback Will Help Find-tune LLMs

Customizing language models (LLMs) through fine-tuning industry-specific datasets is crucial for optimizing their performance in highly specialized domains. However, traditional fine-tuning techniques alone may not suffice. Emerging approaches like Reinforcement Learning from Human Feedback (RLHF), used to train ChatGPT, offer promising solutions.

With RLHF, users provide feedback on LLM responses, which trains a reward system to better align the model with user intents. This is a key reason behind ChatGPT4's improved ability to follow instructions compared to previous models. As we witness the evolution of LLMs, a new generation is on the rise, incorporating innovative techniques that could truly amaze even seasoned AI experts. We might not be ready but the new LLMs are all ready to amaze us.

 

Trend 7: LLMOps Will Revolutionize The Future

Large Language Models (LLMs) have revolutionized various domains with their powerful language understanding and generation capabilities. However, managing and maintaining these complex AI systems in production environments is challenging. Welcoming, LLMOps. They encompass a wide range of tasks, including deploying LLMs, monitoring performance and troubleshooting issues, ensuring reliability, performance and business value delivery.

A major trend in 2024 is the adoption of cloud-based LLMOps solutions, offering scalable, elastic environments with automation and streamlined processes. Additionally, edge computing is gaining traction, as it enables LLMs to be deployed closer to end-users, significantly improving latency and real-time capabilities in AI-driven applications like natural language understanding and voice recognition systems. As LLMs continue to evolve, LLMOps practices must adapt to ensure seamless integration, optimal performance and the realization of their full potential in real-world applications.
 

To Sum Up

The future of LLMs looks quite promising. With innovations like self-validation, self-improvement, prompt engineering, sparse models, plugins and frameworks, these models are paving the way forward. As they become more accurate, efficient and integrated into real-world applications, their potential to revolutionize various industries just keeps growing. That said, the need for robust LLMOps practices to properly manage and optimize these powerful systems remains crucial.

So, the question is, are we ready to embrace this exciting era of advanced language AI?

Explore the cutting-edge trends and advancements shaping AI technology in 2024. Gain valuable insights into how AI innovation is revolutionizing various sectors and stay ahead of the curve with the latest developments. Click here to read more!