Design a negative sampling strategy for training/evaluating our retrieval system. ## Retrieval Task {{retrieval_task}} ## Current Dataset {{dataset_description}} ## Evaluation Goals {{evaluation_goals}} Develop sampling strategies: **Easy Negatives** - Random sampling - Use cases and limitations **Medium Negatives** - BM25 top-k that are irrelevant - Same-topic different answers **Hard Negatives** - Semantic near-misses - Factually incorrect but plausible - Cross-encoder filtered **Mining Implementation** ```python class NegativeMiner: def mine_hard_negatives(self, query: str, positive: str, corpus: List[str], k: int) -> List[str]: pass ``` Include: - Ratio recommendations - Quality filtering - Curriculum learning approach
Negative Sampling Strategy
Design comprehensive negative sampling strategies for retrieval training with easy, medium, and hard negative mining and curriculum learning.
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AnalysisUse Cases
Negative samplingRetrieval trainingEvaluation design
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claude-sonnet-4-20250514gpt-4o
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