ConvMix: A Mixed-Criteria Data Augmentation Framework for Conversational Dense Retrieval

arXiv — cs.CL•Thursday, November 13, 2025 at 5:00:00 AM

The introduction of the ConvMix framework marks a significant advancement in the field of conversational dense retrieval, which seeks to fulfill complex user information needs through multi-turn interactions. Traditional methods often struggle with data scarcity, limiting their effectiveness. ConvMix addresses this issue by employing a mixed-criteria data augmentation strategy that utilizes large language models to enhance the training process. Experimental results indicate that retrievers trained with ConvMix outperform previous baseline methods across five widely used benchmarks, showcasing its superior effectiveness. This development is crucial as it not only improves the retrieval process but also enhances the understanding of user intent, a persistent challenge in conversational search. By integrating quality control mechanisms and diverse sample generation, ConvMix sets a new standard for future research and applications in conversational AI.

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