Bootstrapping ranking models with an LLM judge
Read OriginalThis technical article details an experiment to bootstrap a personalized ranking model for Hacker News article titles. The author uses a Large Language Model (LLM) to label 500 titles based on a complex, user-written preference description. These labels are then used with sentence transformer embeddings and Ridge regression to create a predictive ranking model, achieving a 0.74 Spearman correlation with the LLM's judgments.
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