Large Language Models (LLMs) have made remarkable progress in natural language understanding and synthesis, yet their ability to perform complex reasoning and optimization remains a significant challenge. To address these limitations, innovative approaches are emerging that blend the strengths of LLMs with Traditional AI, creating more capable systems without the need for deep data science expertise. This article explores the challenges of LLM reasoning and optimization and how combining technologies can unlock their full potential.
Reasoning Challenges
LLMs struggle with reasoning, even when faced with small variations in input. A study by Apple, for instance, evaluated the ability of LLMs to solve grade school math questions. Surprisingly, altering the names and numbers in the questions led to a notable drop in performance across several models. Some models saw a performance dip of 4% to 9%, while others experienced a less dramatic impact. In theory, such changes should not have affected the results, suggesting that LLMs are performing pattern matching rather than true reasoning.
Moreover, adding irrelevant information significantly hampers the accuracy of LLMs, sometimes reducing it by as much as 65.7%. These findings highlight a fundamental weakness: LLMs do not inherently reason but instead rely heavily on statistical patterns learned from training data.
Optimization Challenges
Optimization is another area where LLMs face considerable hurdles. In a study focused on spatial task planning, results showed little improvement in optimality between newer and older LLM versions, such as GPT-4, O1-mini, and O1-preview. Although newer models have demonstrated enhanced abilities in generating feasible steps and generalizations, the improvements remain incremental and insufficient for solving complex optimization problems.
Traditional AI Strengths
Where LLMs fall short, Traditional AI shines. Traditional AI excels at these tasks because it uses well-defined algorithms tailored for specific problem domains, enabling it to produce consistent and reliable outputs. Unlike LLMs, which rely on statistical patterns found in the training text, Traditional AI models are explicitly designed to solve reasoning and optimization problems with a clearer understanding of causal relationships. Traditional AI is notably better at tackling both reasoning and optimization tasks, such as customer churn prediction, demand forecasting, loan default prediction, fraud detection, and supply chain optimization. For example, Traditional AI models are well-suited to tasks like financial portfolio management and ad placement optimization, where they can accurately predict and respond to changes.
However, Traditional AI solutions often require data science and machine learning expertise, which can limit their adoption and accessibility. Democratizing access to these tools is essential for broader usage and impact.
BizML - Our Core Innovation
Some companies have begun addressing these challenges by democratizing Traditional AI through technologies like AutoML, automated feature engineering, and LLM integration. Semantic Brain’s BizML takes this concept further by combining domain expertise and feature engineering to create a feedback loop that enhances accuracy by up to 20% and reduces error rates by up to 50%. This combination, along with the incorporation of LLMs, makes powerful AI capabilities accessible to a wider audience.
Using explainable AI methods like SHAP, BizML ensures that outcomes are transparent, enabling teams to make informed decisions and optimally steer processes such as customer conversations.
Overall Solution
A blended approach that integrates LLMs and Traditional AI in a parallel architecture offers an effective solution for solving reasoning and optimization problems, resulting in much faster and cheaper outcomes. For new, "greenfield" projects that need these capabilities, combining LLMs with Traditional AI can yield powerful, personalized products and services. For existing "brownfield" solutions, a careful migration path can help integrate LLMs and Traditional AI with conventional software.
In either case, a "human-in-the-loop" approach is highly recommended during the planning phase, with execution becoming increasingly automated as the system matures.
Outcome and Benefits
The integration of LLMs and Traditional AI within a parallel architecture enhances reasoning and optimization, leading to more personalized and high-quality products and services. By combining the strengths of both technologies, businesses can address their individual limitations, paving the way for a new generation of AI-powered solutions that are more accessible, accurate, and effective. Furthermore, a parallel architecture significantly boosts speed and lowers costs, making new use cases like real-time inbound interactions possible.
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