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Related Questions
- Can a longer prompt lead to biased language models due to the increased likelihood of capturing and perpetuating existing biases?
- How does the length of a prompt affect the model's ability to detect and mitigate bias in its output?
- Can shorter prompts lead to more accurate and fair outputs by reducing the influence of external noise and context?
- What is the optimal prompt length for achieving fairness in LLM outputs, and how does it vary depending on the task and dataset?
- Do longer prompts allow for more nuanced and context-dependent responses, potentially leading to unfair outcomes for certain groups?
- Can the length of a prompt impact the model's ability to recognize and address domain-specific biases and stereotypes?
- How can prompt engineering techniques be used to design prompts that promote fairness and reduce bias in LLM outputs across different lengths?
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