CognitiveBench reveals LLMs suffer representation overlap on joint cognitive tasks due to hierarchical structure; HyCoLLM in hyperbolic space fixes the mismatch and outperforms GPT-4o with far fewer parameters.
org/abs/2402.07092
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LLM-generated synthetic hard negatives for training dense retrievers consistently underperform corpus-mined negatives from BM25 and cross-encoders across 10 BEIR datasets, with non-monotonic gains from scaling the generator from 4B to 30B parameters.
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Modeling Multi-Dimensional Cognitive States in Large Language Models under Cognitive Crowding
CognitiveBench reveals LLMs suffer representation overlap on joint cognitive tasks due to hierarchical structure; HyCoLLM in hyperbolic space fixes the mismatch and outperforms GPT-4o with far fewer parameters.
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Don't Retrieve, Generate: Prompting LLMs for Synthetic Training Data in Dense Retrieval
LLM-generated synthetic hard negatives for training dense retrievers consistently underperform corpus-mined negatives from BM25 and cross-encoders across 10 BEIR datasets, with non-monotonic gains from scaling the generator from 4B to 30B parameters.