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Related Questions
- What is the definition of hallucinations in the context of large language models (LLMs)?
- How do hallucinations differ from overfitting in LLMs, and what are the implications for model performance and trustworthiness?
- Can you provide examples of how hallucinations might occur in an LLM, and how they can be mitigated through proper training and validation?
- How does the concept of hallucinations relate to the concept of model uncertainty in LLMs, and what are the potential consequences of ignoring uncertainty in model predictions?
- In what ways can hallucinations be identified and detected in LLMs, and what are some common indicators of hallucinations in model output?
- What are some strategies for addressing hallucinations in LLMs, and how can they be incorporated into the development and deployment of LLMs in real-world applications?
- How do hallucinations impact the reliability and accountability of LLMs, and what are the implications for human-AI collaboration and decision-making?
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