iPOE generates and optimizes annotation guidelines from explanations to produce interpretable prompts, reporting up to 39% gains over baselines on four datasets with LLM explanations substituting for human ones.
Can Large Language Models Follow Concept Annotation Guidelines? A Case Study on Scientific and Financial Domains
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A new 300-argument German corpus with crowdsourced discrete emotion labels shows LLMs overpredict negative emotions and only partially beat direct binary emotionality prompts.
An iterative moderation framework refines and reuses annotation guidelines to improve LLM annotation accuracy on biomedical NER tasks across GPT, Gemini, and DeepSeek models.
citing papers explorer
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iPOE: Interpretable Prompt Optimization via Explanations
iPOE generates and optimizes annotation guidelines from explanations to produce interpretable prompts, reporting up to 39% gains over baselines on four datasets with LLM explanations substituting for human ones.
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Fearful Falcons and Angry Llamas: Emotion Category Annotations of Arguments by Humans and LLMs
A new 300-argument German corpus with crowdsourced discrete emotion labels shows LLMs overpredict negative emotions and only partially beat direct binary emotionality prompts.
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Refining and Reusing Annotation Guidelines for LLM Annotation
An iterative moderation framework refines and reuses annotation guidelines to improve LLM annotation accuracy on biomedical NER tasks across GPT, Gemini, and DeepSeek models.