Dynamic Context Evolution prevents cross-batch mode collapse in LLMs by combining model self-assessment for idea filtering, embedding-based deduplication, and evolving prompts, yielding zero collapse and consistently richer idea clusters than naive prompting.
Large language model as attributed training data generator: A tale of diversity and bias
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Dynamic Context Evolution for Scalable Synthetic Data Generation
Dynamic Context Evolution prevents cross-batch mode collapse in LLMs by combining model self-assessment for idea filtering, embedding-based deduplication, and evolving prompts, yielding zero collapse and consistently richer idea clusters than naive prompting.