Large language models have several key limitations, including being stochastic, trained on finite data without recent updates, and potentially misled by incorrect prompts. Learn more about the constraints of these powerful AI models.
Table of Contents
Question
Identify all the limitations of Large Language Models
A. These models are stochastic in nature
B. These models are trained on finite data and may not contain recent updates
C. These models never give nonsensical answers
D. These models may be misled by an incorrect input prompt
Answer
A. These models are stochastic in nature
B. These models are trained on finite data and may not contain recent updates
D. These models may be misled by an incorrect input prompt
Explanation
Large language models (LLMs) like GPT-3 are incredibly powerful, but they do have some important limitations:
A) LLMs are stochastic, meaning they have randomness and won’t always produce the exact same output given the same input. This can lead to some inconsistency.
B) LLMs are trained on a fixed dataset up to a certain point in time. Their knowledge doesn’t include information after that cutoff date.
D) An incorrect or misleading input prompt can cause an LLM to generate an incorrect or nonsensical response. The output is heavily dependent on the quality of the input.
However, (C) is not correct – LLMs can and do sometimes produce nonsensical or incorrect responses.
In summary, the key limitations are that LLMs are stochastic, have dated knowledge, and are prompt-dependent, but they are not immune to giving nonsensical answers. Understanding these constraints is important for using LLMs effectively.
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