Retrieve In-Context Examples for LLMs
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A framework to iteratively train dense retrievers that identify high-quality in-context examples.
Iterative training: Trains retrievers using LLM feedback in an iterative loop - retrieved examples that help the LLM answer correctly are used as positive signals.
30-task evaluation: Evaluated across 30 NLP tasks showing consistent improvements over random or similarity-based retrieval.
Pattern-similar examples: Confirms that examples sharing abstract patterns (not just surface similarity) are most useful for ICL.
Scale-invariant gains: Improvements are consistent across model sizes, suggesting dense retrieval is a robust ICL enhancement that transfers across model scales.
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