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Related Questions
- What are common overused tropes that human evaluators can look out for during LLM training?
- How can human evaluators' feedback on LLM responses be systematically incorporated into the model's training process?
- Can you discuss the types of feedback human evaluators should provide when flagging tropes, such as pointing out overly clichéd plots or tired character archetypes?
- What tools or annotation frameworks would human evaluators need to help them accurately and consistently mark trope examples for training purposes?
- Would employing human evaluators alongside language model performance metrics enable a more accurate representation of model capabilities, as compared to relying on AI feedback only?
- Would feedback from multiple evaluators yield more nuanced or diversified ratings, particularly if working within a consensus framework or averaging ratings for added fairness and accuracy?
- As human evaluators scrutinize LLM-generated texts, how will you help create an updated evaluation method based on best practices?
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