What are the challenges and limitations of large language models?
While there are many advantages to using LLMs, there are also several challenges and limitations :
Development costs : To run, LLMs generally require large quantities of expensive graphics processing unit hardware and massive data sets.
Operational costs : After the training and development period, the cost of operating an LLM for the host organization can be very high.
Complexity : With billions of parameters, modern LLMs are exceptionally complicated technologies that can be particularly complex to troubleshoot.
Bias : A risk with any AI trained on unlabeled data is bias, as it's not always clear that known bias has been removed.
Explainability : The ability to explain how an LLM was able to generate a specific result is not easy or obvious for users.
Hallucination : AI hallucination occurs when an LLM provides an inaccurate response that is not based on trained data.
Glitch tokens : Maliciously designed prompts that cause an LLM to malfunction, known as glitch tokens, are part of an emerging trend since 2022.
