Recent progress in Natural Language Understanding (NLU) is driving fast-paced advances in Information Retrieval (IR), largely owed to fine-tuning deep language models (LMs) for document ranking. While remarkably effective, the ranking models based on these LMs increase computational cost by orders of magnitude over prior approaches, particularly as they must feed each query-document pair through a massive neural network to compute a single relevance score. To tackle this, we present ColBERT, a novel ranking model that adapts deep LMs (in particular, BERT) for efficient retrieval. ColBERT introduces a late interaction architecture that independently encodes the query and the document using BERT and then employs a cheap yet powerful interaction step that models their fine-grained similarity. By delaying and yet retaining this fine-granular interaction, ColBERT can leverage the expressiveness of deep LMs while simultaneously gaining the ability to pre-compute document representations offline, considerably speeding up query processing. Beyond reducing the cost of re-ranking the documents retrieved by a traditional model, ColBERT's pruning-friendly interaction mechanism enables leveraging vector-similarity indexes for end-to-end retrieval directly from a large document collection. We extensively evaluate ColBERT using two recent passage search datasets. Results show that ColBERT's effectiveness is competitive with existing BERT-based models (and outperforms every non-BERT baseline), while executing two orders-of-magnitude faster and requiring four orders-of-magnitude fewer FLOPs per query.
ColBERT introduces a novel ranking model leveraging late interaction over BERT, significantly improving the efficiency and effectiveness of passage search in Information Retrieval systems.
It features separate encoders for queries and documents, using contextual embeddings and a late interaction mechanism to reduce computational costs while maintaining retrieval effectiveness.
The model has been extensively evaluated, demonstrating substantial improvements in execution speed and competitiveness in retrieval effectiveness compared to existing BERT-based models.
The introduction of ColBERT aims to balance the computational demands of deep language models with the needs for efficient and effective document ranking, opening pathways for future advancements in Information Retrieval.
The surge in applying deep language models (LMs) to document ranking tasks has notably enhanced the Natural Language Understanding (NLU) capabilities within Information Retrieval (IR) systems. However, the computational demands of these models, particularly when using BERT for evaluating query-document pairs, introduce significant challenges in terms of latency and resource consumption. To address these issues, the paper introduces ColBERT, a novel ranking model leveraging late interaction over BERT to offer a balance between efficiency and retrieval effectiveness.
ColBERT innovates by encoding queries and documents independently into sets of contextual embeddings using BERT, followed by a late interaction mechanism. This approach significantly differs from traditional models where embeddings for the query and the document are either combined early or require exhaustive pairwise interaction, leading to increased computational costs.
ColBERT is extensively evaluated on two recent passage search datasets, demonstrating that it maintains competitive effectiveness with existing BERT-based models while significantly reducing computational costs.
The introduction of ColBERT provides a promising avenue for integrating deep language models into IR systems without incurring prohibitive computational costs. Its efficient and effective design presents a significant step forward in addressing the latency challenges associated with deep LMs like BERT in document ranking tasks. Future work may explore the extension of the ColBERT architecture to other forms of interaction and the application of the model to additional IR tasks beyond passage search.
In summary, ColBERT elegantly reconciles the trade-off between efficiency and effectiveness prevalent in current deep LM-based IR systems, offering a scalable solution for incorporating advanced NLU capabilities into real-time search engines.