Conventional Contrastive Learning Often Falls Short: Improving Dense Retrieval with Cross-Encoder Listwise Distillation and Synthetic Data (2505.19274v1)

Abstract: We investigate improving the retrieval effectiveness of embedding models through the lens of corpus-specific fine-tuning. Prior work has shown that fine-tuning with queries generated using a dataset's retrieval corpus can boost retrieval effectiveness for the dataset. However, we find that surprisingly, fine-tuning using the conventional InfoNCE contrastive loss often reduces effectiveness in state-of-the-art models. To overcome this, we revisit cross-encoder listwise distillation and demonstrate that, unlike using contrastive learning alone, listwise distillation can help more consistently improve retrieval effectiveness across multiple datasets. Additionally, we show that synthesizing more training data using diverse query types (such as claims, keywords, and questions) yields greater effectiveness than using any single query type alone, regardless of the query type used in evaluation. Our findings further indicate that synthetic queries offer comparable utility to human-written queries for training. We use our approach to train an embedding model that achieves state-of-the-art effectiveness among BERT embedding models. We release our model and both query generation and training code to facilitate further research.

Enhancing Dense Retrieval through Cross-Encoder Listwise Distillation and Synthetic Data Integration

Dense retrieval models, crucial for numerous information retrieval applications, have benefited substantially from advancements in embedding methods and model architectures. This paper by Tamber et al. examines the often-observed limitations of conventional InfoNCE contrastive learning in the fine-tuning of dense retrievers and proposes an alternative approach that integrates cross-encoder listwise distillation with diverse synthetic query data.

Core Findings and Methodologies

The paper presents evidence that while fine-tuning dense embedding models using corpus-specific data is known to enhance retrieval effectiveness, reliance solely on the InfoNCE loss can paradoxically reduce effectiveness in state-of-the-art models. This finding challenges the conventional preference for contrastive learning in refining retrieval models and suggests inherent limitations.

To address these challenges, the authors revisit cross-encoder listwise distillation, highlighting its underexplored potential. By leveraging scores from a teacher model, listwise distillation provides richer relevance signals compared to the binary nature of contrastive learning. This method allows for nuanced training that effectively captures the complexities of passage relevance. Empirical evaluations demonstrate that listwise distillation, as opposed to contrastive learning alone, results in consistent improvements across diverse datasets.

The paper further investigates the utility of synthetic query generation as a means of training data augmentation. By synthesizing queries in various forms (claims, keywords, questions) without dependence on specific query examples from the target datasets, the authors show that a diverse training set is more beneficial than single query-type datasets. This approach not only amplifies the available training data but also enhances generalization and robustness in model performance.

Numerical Outcomes and Model Implications

The paper reports strong numerical outcomes, particularly noting that retrieval models fine-tuned with a combination of cross-encoder listwise distillation and diverse synthetic queries achieve superior effectiveness compared to those relying solely on traditional methods. The model trained using this method, referred to as "cadet-embed-base-v1," establishes new benchmarks for state-of-the-art efficacy among BERT embedding models.

Theoretical and Practical Implications

Theoretically, the research highlights a crucial paradigm shift in the training of dense retrieval models. By demonstrating the limitations of contrastive learning and the efficacy of distillation-based methods, it opens new avenues for more sophisticated and effective training methodologies. The integration of synthetic data also underscores broad applicability, indicating that diverse training inputs can significantly enhance model generalization.

Practically, the release of the trained model and supporting resources on public platforms like Hugging Face and GitHub provides a valuable toolkit for the research community. It facilitates further exploration and adoption of these methods, potentially influencing future developments in AI-driven retrieval systems.

Future Directions

Speculative considerations for the future of AI in dense retrieval could explore scaling these methods to larger LLMs and incorporating multilanguage capabilities. The current methodologies might also benefit from integration with more advanced generative models for even more diverse training datasets. Moreover, extending the applications of cross-encoder distillation in other domains outside of retrieval tasks could be a promising direction for further research.

In conclusion, Tamber et al.'s work presents compelling evidence for the advantages of listwise distillation and synthetic data in dense retrieval model training. Their contributions not only offer immediate improvements in retrieval tasks but also lay a foundation for future explorations in model training techniques. This work represents a meaningful step forward in the ongoing refinement of information retrieval methodologies.