Papers with weak student models

1 papers
MTA4DPR: Multi-Teaching-Assistants Based Iterative Knowledge Distillation for Dense Passage Retrieval (2024.emnlp-main)

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Challenge: Existing studies have shown the effectiveness of knowledge distillation in DPR, but there is a performance gap between the teacher and the distilled student.
Approach: They propose an iterative knowledge distillation method which transfers knowledge from teacher to student with help of multiple assistants in an iterated manner.
Outcome: The proposed method achieves state-of-the-art performance among models with same parameters on multiple datasets and is competitive when compared with larger models.

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