ARTICLE: Annotator Reliability Through In-Context Learning
Using LLMs to identify high-quality human annotators by checking if their labels are consistent with AI predictions—helping build better training data while preserving diverse …
Sujan Dutta
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1 min readProRefine: Inference-Time Prompt Refinement with Textual Feedback
ProRefine automatically improves AI prompts during inference by having one AI agent give feedback to refine another agent's prompts—boosting accuracy by 3-37% and helping smaller …
Deepak Pandita
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1 min readRater Cohesion and Quality from a Vicarious Perspective
Asking people to predict how others with different political views would label content reveals hidden biases and improves data quality for content moderation AI.
Deepak Pandita
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1 min read