The Paradox of AI: Brilliance and Limitations of Large Language Models
Google DeepMind's AlphaProof and AlphaGeometry 2 solved 4 out of 6 problems from this year's International Mathematical Olympiad, matching a silver medalist. This competition features problems so difficult that only the top pre-college mathematicians can solve them.
Yet, Large Language Models (LLMs) often struggle with simple tasks:
* Which number is bigger, 9.9 or 9.11?
* Counting the 'R's in "Strawberry"
* Simple family relationships
The ARC challenge exemplifies this paradox, presenting questions that young children can answer but which confound LLMs.
Having used LLMs extensively since ChatGPT's release, I can attest to their brilliance and limitations, causing challenges in practical applications.
Key Considerations for LLM Integration
To effectively harness LLMs, understanding their strengths and limitations is crucial. While the landscape evolves rapidly, some fundamental principles persist:
1. Pattern Recognition and Generalization
LLMs excel at patterns encountered in their training set and generalizations based on those patterns. They handle most written materials well, such as books, news articles, Wikipedia, and technical documentation. However, their learning materials are not as diverse as the experiences human children encounter. The gap between "book smarts" and "street smarts" is evident in technical domains where LLMs may possess knowledge but lack practical experience.
2. Contextual Understanding Challenges
LLMs struggle with larger context understanding, partly because users must provide comprehensive context. They perform well with clearly articulated problems and relevant information. However, real-life problem identification involves processing extensive multi-modal input and internal reflections. Without this, LLMs cannot replace humans. For example, a program manager might identify project tweaks from various interactions and task statuses, which LLMs struggle to replicate.
3. Solution Depth and Creativity
LLMs lack depth in solution-finding. While they can identify issues and propose solutions like a junior or senior engineer, they don't think several levels deeper to produce flexible, adaptable, and robust solutions. This might be due to their training data focusing too much on immediate problem-solving. Consequently, I often generate improvement ideas and use LLMs to implement them. Future LLM versions might reduce the need for human-driven direction.
The Kavia Approach: Harnessing LLMs Responsibly
At Kavia, we recognize that while LLMs offer powerful capabilities, they are far from replacing human expertise in complex tasks. Our approach centers on:
1. Leveraging LLMs for their strengths
2. Maintaining humans as the primary decision-makers
3. Implementing robust human-in-the-loop processes
We are developing systems that utilize LLMs for background tasks while ensuring full human oversight for critical decisions and final outputs.
