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Questions and Answers
What is a limitation of LLMs due to their training data?
What is a possible consequence of insufficient knowledge on certain topics in LLMs?
What is a challenge posed by the behavior of large, complex LLMs?
What is a limitation of LLMs in terms of interactive actions?
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What can be a consequence of the training data used for LLMs?
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What is a limitation of LLMs in terms of previous conversations?
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Study Notes
Limitations of Large Language Models (LLMs)
- Outdated knowledge: LLMs rely solely on their training data, making them unable to provide recent real-world information without external integration.
Functional Limitations
- Inability to take action: LLMs cannot perform interactive actions such as searches, calculations, or lookups, severely limiting their functionality.
Risks of LLMs
- Hallucination risks: Insufficient knowledge on certain topics can lead to the generation of incorrect or nonsensical content by LLMs if not properly grounded.
Biases and Transparency Issues
- Biases and discrimination: LLMs can exhibit biases that can be religious, ideological, or political in nature, depending on the data they were trained on.
- Lack of transparency: The behavior of large, complex models can be opaque and difficult to interpret, posing challenges to alignment with human values.
Contextual Understanding Limitations
- Lack of context: LLMs may struggle to understand and incorporate context from previous prompts or conversations.
- Contextual memory limitations: LLMs may not remember previously mentioned details or may fail to provide additional relevant information beyond the given prompt.
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Description
Explore the various limitations of Large Language Models (LLMs), including reliance on outdated knowledge, inability to take actions, risks of hallucinations, biases, and discriminations. Learn about the challenges associated with utilizing LLMs in real-world applications.
