ColBERTv2: Effective and Efficient Retrieval via Lightweight Late Interaction (2112.01488v3)

Abstract: Neural information retrieval (IR) has greatly advanced search and other knowledge-intensive language tasks. While many neural IR methods encode queries and documents into single-vector representations, late interaction models produce multi-vector representations at the granularity of each token and decompose relevance modeling into scalable token-level computations. This decomposition has been shown to make late interaction more effective, but it inflates the space footprint of these models by an order of magnitude. In this work, we introduce ColBERTv2, a retriever that couples an aggressive residual compression mechanism with a denoised supervision strategy to simultaneously improve the quality and space footprint of late interaction. We evaluate ColBERTv2 across a wide range of benchmarks, establishing state-of-the-art quality within and outside the training domain while reducing the space footprint of late interaction models by 6--10$\times$.

• The paper introduces a residual compression mechanism that cuts storage requirements by 6–10× while preserving retrieval quality.
• The paper employs denoised supervision via cross-encoder distillation and hard-negative mining to enhance meaningful token-level interactions.
• The paper demonstrates state-of-the-art performance on in-domain tasks like MS MARCO and robust out-of-domain generalization across 22 of 28 benchmarks.

An Expert Overview of ColBERTv2: Effective and Efficient Retrieval via Lightweight Late Interaction

The field of neural information retrieval (IR) has seen significant advancements, particularly in search and knowledge-intensive language tasks. Traditional neural IR methods often rely on encoding queries and documents into single-vector representations, facilitating relevance evaluation through simple vector comparisons. However, late interaction models, as introduced in ColBERT, have provided an alternative approach by representing queries and documents at a token level, resulting in multi-vector representations that allow for richer interactions. Despite their improved expressiveness, these models typically suffer from a substantial increase in storage requirements.

ColBERTv2's Contributions

ColBERTv2 addresses the limitations of existing late interaction models by introducing two primary innovations: a resilient compression mechanism and denoised supervision. These innovations enable ColBERTv2 to improve the quality of retrieval while significantly reducing storage needs.

1. Residual Compression:
   • ColBERTv2 employs a compression mechanism that leverages the regularity of token representations in its semantic space. The method introduces residual compression, encoding each token’s vector as a combination of an index to a centroid and a quantized residual vector.
   • This methodology achieves a 6–10× reduction in storage space, bringing the space requirements of late interaction models closer to those of single-vector models.
2. Denoised Supervision:
   • ColBERTv2 enhances its training by distilling from a cross-encoder, thus focusing on meaningful token-level interactions. This approach ensures that the model benefits from more expressive interaction signals without overfitting to noise.
   • The combination of cross-encoder distillation with hard-negative mining significantly boosts ColBERTv2's retrieval quality, achieving state-of-the-art results across multiple benchmarks.

Evaluation and Results

ColBERTv2 sets new standards in retrieval quality, both within its training domain on datasets like MS MARCO and in zero-shot scenarios across diverse out-of-domain datasets. The model outperforms previous late interaction and single-vector systems by notable margins, demonstrating particular strength in handling natural search queries compared to traditional document similarity tasks.

1. In-Domain Performance:
   • On the MS MARCO Passage Ranking task, ColBERTv2 achieves the highest Mean Reciprocal Rank (MRR@10) among standalone retrievers, underscoring the effectiveness of its enhanced training and representation strategies.
2. Out-of-Domain Generalization:
   • Evaluated on a variety of benchmarks, including BEIR and LoTTE, ColBERTv2 demonstrates robust generalizability, surpassing other models on 22 out of 28 tests. This performance highlights its capacity to tackle a wide range of topics and query structures.

Practical and Theoretical Implications

The introduction of ColBERTv2 reinforces the potential of late interaction approaches in neural IR. It strikes a balance between expressiveness and efficiency, challenging the notion that single-vector representations are inherently more scalable. The innovations in ColBERTv2 could pave the way for broader application in open-domain question answering and fine-grained topic retrieval.

Future Directions

ColBERTv2's advancements open several avenues for exploration. Future research could delve into further optimizing compression techniques and exploring other forms of supervision to maximize retrieval quality. The methods developed in ColBERTv2 could also serve as a foundation for improving related downstream NLP tasks, leveraging its efficient and scalable retrieval framework.

ColBERTv2 exemplifies the ongoing evolution of information retrieval methodologies, integrating sophisticated compression techniques with robust interaction modeling to address scalability without sacrificing quality. This work illustrates the effectiveness of tailored neural architectures in overcoming fundamental challenges in the field of information retrieval.