Papers with hard negative sampling approach

1 papers
On Synthetic Data Strategies for Domain-Specific Generative Retrieval (2025.acl-long)

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Challenge: Generative retrieval models can be used to generate ranked lists of potentially relevant document identifiers for a user query.
Approach: They propose a synthetic data generation strategy for a two-stage training framework that focuses on learning to decode document identifiers from queries and a strategy for mining hard negatives based on initial model's predictions.
Outcome: The proposed model can generate ranked lists of potentially relevant document identifiers for a user query and then refine ranking through preference learning.

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