LLM Trends in the Making: What to Expect in 2024

2023 was a landmark year for AI, with Large Language Models (LLMs) like ChatGPT becoming a household name. They evolved to be smarter, more cost-effective, and quicker, driven by enhancements in knowledge breadth, multimodal capabilities and expanded context windows. These models have catalyzed productivity in business, from enhancing customer service through sophisticated chatbots to spurring content creation.

This article looks ahead to 2024, forecasting future LLM advancements, and their impact on sales and marketing use cases. We anticipate LLMs not just augmenting existing abilities but also opening new avenues for innovation and efficiency.

The Dawn of Multimodal Mastery: New Frontiers in Creativity and Reasoning

Current LLMs struggled with logical reasoning (with 60% accuracy, as the graph below illustrates), biases, hallucinations, and a focus on text-based tasks.

LLMs like GPT-5, LLAMA 3, and Gemini Ultra are improving logical reasoning through symbolic integration and causal learning, reducing biases via data filtering and fairness-aware training. Additionally, they’re expanding into multimodal learning, handling text, audio, images, videos, and code. These developments suggest a future of more adept and dependable general-purpose models.

Small Yet Mighty: The Emerging Influence of Small Language Models (SLMs)

While Large Language Models (LLMs) receive acclaim, Small Language Models (SLMs) are often more fitting for businesses focusing on efficiency and security. LLMs, despite their broad capabilities, can harbor biases and inaccuracies due to vast training data. They might not align with specific business needs and raise data security issues.

On the other hand, SLMs offer a sustainable, secure alternative, requiring less computational power and well-suited for on-premise deployment. Their smaller scale doesn’t diminish their effectiveness as they can be customized to meet distinct business goals. Models like Mistral-7B, Falcon-7B, and Phi 2 (2.7 billion parameters) demonstrate SLMs’ adaptability and efficiency. They provide a focused, efficient AI solution, achieving a balance between performance, security, and personalization.

Niche Know-How: The Rise of Domain-Specific Fine Tuned LLMs

General AI models often fall short in niche areas, lacking the required precision and accuracy. Domain-specific, fine-tuned LLMs address this by offering specialized knowledge. For instance, Google’s Med-Palm 2, honed with medical data, accurately responds to medical inquiries, achieving 86.5% score on the MedQA dataset (compared to GPT 3.5’s 60.2%, as the graph below shows). Similarly, BloombergGPT, tailored for financial data, exemplifies the efficacy of such targeted LLMs.

Faster, Cheaper, Smarter: The Economic Revolution of LLMs

In 2020, using GPT-2 to analyze a million product reviews cost about $10,000. Today, GPT-4 does the same for around $3,000. This decrease in cost, illustrated by the accompanying graph, has made LLMs more accessible.

However, processing a million reviews still takes about 17 hours. As LLMs evolve, we anticipate further reductions in both cost and processing time. This advancement will enhance LLM accessibility, allowing businesses to analyze large data sets more efficiently and cost-effectively.

AI Agents: The New Frontier in Intelligent Automation

AI agents in 2023 made notable progress. They formulate action plans, store data, select appropriate tools, and execute these plans. Consider a research-focused AI agent. When presented with a query, it independently navigates the web, aggregating and synthesizing data from varied sources. It then crafts a clear, informed response, delivering it via email. This process enhances research efficiency, providing timely and thorough information. The number of AI agents is projected to surge in 2024, driven by their capability in automating tasks intelligently.

Retrieval Augmented Generation (RAG): Bridging the gap between LLMs and user data

Retrieval Augmented Generation (RAG) has significantly improved Large Language Models’ (LLMs) accuracy by retrieving relevant information from private user data. While future model architectures may support unlimited contextual windows making RAG redundant,, this advancement might take a year or two. Thus, in 2024, RAG systems are likely to gain widespread popularity. They currently stand as the leading method for deriving insights from private data, a need that is growing rapidly.

Revolutionizing Automations: The Power of LLMs in Streamlining Sales & Marketing

While 2023 saw automation of content generation, automation is expected to spread further in 2024.

● Analytics: LLMs can facilitate the extraction of actionable insights from raw data by automating various analytical tasks, including data preparation, analytics planning, query generation/execution, and insights generation.

● Customer Support: In 2024, LLMs will be further automating modern customer service, offering 24/7 personalized support, efficient problem-solving, and multilingual capabilities.

● Marketing Automation: LLM-powered agents will be further transforming marketing through personalized, scalable campaigns and efficient data analysis.

● Workflow Automation: LLMs are transforming workflow automation using intelligent agents.

We will delve deeper into these automations in our upcoming article. For 2024, expect Large Language Models (LLMs) to revolutionize various industries with the advancements discussed. These developments are poised to usher in a new era of innovation and efficiency, particularly in sales and marketing.