10 Trends in Large Language Model(LLMs) Dev Business Owners Need To Watch in 2024

Artificial intelligence (AI), also known as machine learning, has been a part of the technology landscape for a very long time. It’s the advent of large language models that has captured the attention of both professionals and business owners in the field.

Recent findings from McKinsey reveal that a significant portion-specifically, one-third-of businesses are now integrating generative AI into at least one facet of their operations. This highlights a growing demand for AI and machine learning engineers to meet market needs.

Understanding the current trends in LLMs will help businesses make informed decisions about the adoption of these models in their future ventures. Keeping abreast of the latest trends allows AI developers to continuously refine their expertise in order to meet industry demands.

What exactly is the evolution of LLMs like? This article explores the evolution of large language model, from the transition to multimodal input through the burgeoning landscape of open-source software and the increasing cost-effectiveness.

1. LLMs Will Become Increasingly Multimodal

Multimodality is a significant trend in the development and implementation of Language and Learning Models. AI vendors are increasingly creating AI systems that can process and generate responses in various formats, including text, audio, video, and images.

In a recent conversation between Sam Altman, and Bill Gates where Altman stressed the importance of “multimodality”, this shift was highlighted. OpenAI’s GPT-4V was introduced last year, which further accentuated the trend by allowing users to integrate image inputs into ChatGPT interactions.

Google has also adopted this paradigm, introducing a set of multimodal LLMs called Gemini. Demis Hassabis is the CEO and cofounder of Google DeepMind. He describes Gemini as an LLM that was “crafted from its inception to be multimodal.” It supports inputs such as text, code, audio, images, videos, and more.

2. The Gap Between Open and Closed-Source Models will Continue to Close

Open-source LLMs are gaining ground in the world of cutting-edge development. While solutions like ChatGPT, Claude and Bard have been around for a long time, the gap between them and their closed-source counterparts is slowly narrowing.

Meta recently made waves when it unveiled Code Llama70B, a finely-tuned version of Llama 2 that is tailored specifically for coding. It was impressively better than GPT 3.5 on the HumanEval benchmark. It scored 53% accuracy, compared to GPT’s 48.1%.

Mistral AI also entered the fray by launching 8x7B. This robust language model boasts a staggering 46,7 billion parameters. This powerhouse is claimed to deliver six-times faster inference speeds than Llama 70B, and it’s said to match or exceed GPT 3.5 for most standard benchmarks.

Although these LLMs are not yet at the level of GPT-4 or other top contenders, there is a noticeable increase in the number of LLMs available for enterprise consideration.

Also Read: Data models: PDM vs. CDM vs. LDM

3. The Rise of Small Language Models

AI, one cannot overlook the formidable obstacle posed by the exorbitant expenses associated with training and deploying AI models. For myriad organizations, this financial hurdle has served as a formidable deterrent to embracing AI technologies. Consider, for instance, the staggering price tag attached to the training of sophisticated language models like GPT 3.5, which could easily surpass the daunting sum of $4 million. Undoubtedly, such an investment demands considerable deliberation from any organization.

In light of this fiscal reality, numerous AI vendors are now exploring the development of compact language models – abbreviated as LLMs – characterized by a reduced parameter count. These LLMs boast the capacity to execute inference tasks with remarkable efficiency while consuming fewer computational resources.

As we navigate through 2024, a plethora of such models has emerged, each carving its niche in the expansive landscape of artificial intelligence. One noteworthy example is Stability AI’s recent unveiling of the Stable LM, a diminutive language model featuring a modest 1.6 billion parameters. Trained on a colossal dataset comprising 2 trillion tokens, this model exhibits proficiency across multiple languages, encompassing English, Spanish, German, Italian, French, Portuguese, and Dutch.

Equally compelling is Microsoft’s Phi-2, introduced to the world stage in December 2023. With an impressive parameter count of 2.7 billion, this model showcases unparalleled prowess in reasoning and linguistic comprehension. Surpassing models of up to 25 times its size, Phi-2 owes its superior performance to an intricately curated dataset.

These recent unveilings merely scratch the surface of an escalating trend: the emergence of models engineered for heightened efficiency compared to their predecessors, the conventional LLMs.

4. Language Models Are Going to Become Less Expensive

Three factors are paramount: “perplexity,” “burstiness,”and “predictability.” Perplexity measures the complexity of text while burstiness examines the ebbs-and-flows of sentence structures. Predictability is the art of keeping the reader on their toes by making the next sentence unpredictable. Humans are masters at weaving narratives that combine long expositions with short interludes to create a tapestry full of linguistic intrigue. AI tends to use a uniformed expression.

Vendors are working together to reduce costs in the AI space. This includes the training and deployment of Large Language Models. Recent developments, such as OpenAI’s decision of slashing prices on its acclaimed GPT 3.5 turbo model, are a good example of this effort. Just a few weeks ago, OpenAI made headlines when it announced a 50% reduction in input costs to $0.0005 per 1,000 tokens, along with a 25% drop in output costs to $0.0015.

OpenAI is not the only player in this cost-cutting war. Anthropic, a prominent AI entity, has also embarked upon a journey to reduce expenses regarding its flagship proprietary LLM Claude 2.

These significant price adjustments are occurring in tandem with the advancements in cost-efficient Scalable Language Models. It is increasingly likely that the overall financial outlay for these innovative solutions will be on a downward trend in the near future.

5. More Will Experiment with Direct Preference Optimization as an Alternative to RLHF

Over the years, the technique of reinforcement learning from user feedback (RLHF), has been used to refine machine learning algorithms in order to match the preferences of humans.

Recent findings from Stanford have introduced a compelling alternative: direct preference optimization (DPO), a method that is poised to gain significant momentum among language model (LLM), providers.

RLHF is a method that allows developers to build a reward system based on human feedback. This helps them fine-tune models according to the preferences of humans. Stanford’s method, on the other hand, offers a more streamlined way to train language models to adhere with preferences without the long process of reinforcement learning.

“DPO establishes correlations between language model policies, and reward functions. This allows direct training of language models to meet human preferences, without the need for reinforcement or generality. DPO’s performance is comparable or better than existing RLHF algorithms with minimal hyperparameter adjustment, according to the study.

6. The Move to Autonomous Agents

Autonomous agents are a growing element in the LLM development world. In the last year, entities like AutoGPT have been in the spotlight. They are able to interact with language models, such as GPT 3.5 or GPT-4 and execute tasks without human intervention.

These agents are capable of constructing websites or conducting market analyses without the need for manual input from users. While their arrival brings new opportunities for businesses, it also presents a variety of challenges, especially in the area of cybersecurity.

The Center for AI Safety has raised alarms about the possibility that nefarious actors could engineer rogue autonomous agents. They cite an incident in which a developer harnessed GPT-4 and created ChaosGPT, a AI entity with the ominous goal of “destroying humanity.” The attempt was quickly thwarted but it highlights the vulnerability of such tools.

7. Robotics-Focused Vision-Language Action Models Will Pick Up Speed

The advancement of artificial intelligence and robotics have been closely linked for many years. The development of humanoid robotics like Sophia by Hanson Robotics to the most recent developments that indicate a surge in interest from AI companies venturing in this domain.

Recent reports by Business Insider dated January 2024 reveal major plans from major players such as Microsoft and OpenAI. They contemplate a joint $500 million investment into Figure AI, an emerging startup in the robotics industry.

The introduction of Google DeepMind’s Robotics Transformer 2(RT-2) marked a milestone in the past year. This innovative model is dubbed the vision-language action system (VLA), and aims to improve robots’ understanding and execution of tasks.

RT-2 is based on a Language-Generation Model to orchestrate motion control, enabling robots to decipher instructions effectively. These commands can be complex, such as determining the relative size of objects or numerical cues.

It’s clear that AI companies will continue to push boundaries as they strive to improve the symbiotic relation between artificial intelligence (AI) and physical machinery.

8. More AI Vendors Will Offer Users Custom Chatbots

A notable trend in the ever-evolving world of AI engineering is towards personalization. A growing number of providers are now offering customizable chat assistants.

OpenAI’s GPTs, which are essentially bespoke versions of ChatGPT, will be introduced in 2023. They can be distributed to users via the newly launched GPT Store.

Hugging Face offers users the option to create their own chatbots using the Hugging Chat Assistant. Users can choose from a variety of open LLM options, and customize them by adding a name, avatar and description.

It is not surprising that more providers will adopt this approach, given that other entities such as Bytedance are exploring custom chatbots.

9. Generative AI Will Be Used in More Consumer Apps

In order to improve the accessibility of AI-generated insights in both consumer-oriented products and enterprise-centric ones, a growing number of vendors integrate LLMs such as ChatGPT.

Aim Research projects that by 2024, around 40% of enterprise applications will have embedded conversational AI. Meanwhile, 70% of practical applications will be able to deliver real-time results by 2030.

At present, generative AI can be found in a wide range of popular products. In June 2023, Grammarly enhanced its proofreading software with generative AI, giving users the ability to create content at their own convenience.

HubSpot’s HubSpot CRM has been enhanced with AI-powered functionality, including a Content assistant that is adept at creating blog titles, outlines and various content formats, such as blog posts.

Also Read: What is Sustainable Technology?

10. Retrieval Augmented Generation Will Make LLMs Smarter

Researchers are increasingly interested in retrieval augmented generating (RAG) as a way to enhance language model capabilities.

Researchers can establish a link between the model and a knowledge base external to RAG. This gives the AI model access real-time data and vast databases of data, allowing it to provide more informed answers to user questions.

Pinecone’s research shows that using GPT-4 along with RAG results in a 13% improvement in the quality of answers, even for information already embedded in the language model. This means that the quality improvement would be even greater when dealing with queries involving sensitive or private information.


Language Model Models are still in their infancy in the world of technology. However, their capabilities are rapidly advancing. As multimodality becomes more popular and LLMs become more affordable and efficient, the barriers for adopting AI are decreasing.

These advancements are far from reaching artificial general intelligence (AGI), but they are making progress, and we expect a surge of adoption in 2024 as more applications emerge.

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