Inherent limitations of LLMs
LLMs have shown remarkable capabilities in generating code, but they also possess inherent limitations that can significantly impact the quality and reliability of the output.
Core limitations
Here are some of the limitations that LLMs have:
- Lack of true understanding: While LLMs can generate syntactically correct code, they lack a deep understanding of the underlying concepts, algorithms, and problem domains. This can lead to suboptimal or incorrect solutions.
- Hallucinations: LLMs can generate plausible-sounding but incorrect or nonsensical code, often referred to as “hallucinations.” This can be particularly problematic in critical applications.
- Dependency on training data: The quality of LLM-generated code is heavily reliant on the quality and diversity of the training data. Biases or limitations in the training data can be reflected in the generated code.
- Difficulty with complex logic: LLMs often struggle with...
