Answered

Which is not a limitation of using closed-source LLMs?

A. There can be data privacy concerns preventing you from sharing data with the LLM creators.
B. Consistently using closed-source LLMs for various use cases can incur high costs.
C. Closed-source LLMs may have limitations in terms of customization and fine-tuning for specific applications.
D. Closed-source LLMs are always less accurate and perform poorly compared to open-source LLMs.



Answer :

GinnyAnswer

Final answer:

Closed source LLMs have limitations related to data privacy concerns, high costs, and customization restrictions.


Explanation:

Closed source LLMs refer to machine learning models whose source code is not openly available for viewing or modification. Here are the limitations of using closed source LLMs:

  1. Data privacy concerns: Sharing data with closed-source LLM creators may pose risks to data privacy.
  2. High costs: Using closed source LLMs for multiple cases can lead to significant expenses.
  3. Limitations in customization: Closed-source LLMs may have restrictions in customizing and fine-tuning for specific applications.

Learn more about Closed source LLM limitations here:

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