Despite their capabilities, LLMs face several limitations when it comes to restructuring and converting structured documents. One significant drawback is variability in output quality, where identical prompts may yield different results due to the stochastic nature of LLMs. This inconsistency can be problematic in applications requiring high reliability. To address this, developers can implement prompt engineering techniques that standardize inputs and reduce ambiguity, thereby increasing the likelihood of consistent outputs.
Another limitation is the risk of hallucination, where LLMs generate plausible but incorrect or nonsensical information. This is particularly concerning in structured document processing, where accuracy is paramount. To mitigate this risk, it is essential to incorporate validation mechanisms that cross-check LLM outputs against known data or predefined rules. Additionally, using zero-shot or few-shot learning approaches can help LLMs better understand the context and requirements of specific tasks, reducing the likelihood of generating erroneous outputs.
Furthermore, LLMs may struggle with complex or non-standard document formats that deviate from their training data. To overcome this, it is beneficial to provide comprehensive examples and detailed instructions in prompts, ensuring that LLMs have the necessary context to perform the required transformations accurately.