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LLM research suggests input diversity boosts accuracy more than output diversity

A new research paper explores Test-Time Augmentation (TTA) for Large Language Models (LLMs), proposing that diversifying input data is more compute-efficient for accuracy gains than diversifying output reasoning paths. …