Contextual Document Embeddings (2410.02525v1)

Abstract: Dense document embeddings are central to neural retrieval. The dominant paradigm is to train and construct embeddings by running encoders directly on individual documents. In this work, we argue that these embeddings, while effective, are implicitly out-of-context for targeted use cases of retrieval, and that a contextualized document embedding should take into account both the document and neighboring documents in context - analogous to contextualized word embeddings. We propose two complementary methods for contextualized document embeddings: first, an alternative contrastive learning objective that explicitly incorporates the document neighbors into the intra-batch contextual loss; second, a new contextual architecture that explicitly encodes neighbor document information into the encoded representation. Results show that both methods achieve better performance than biencoders in several settings, with differences especially pronounced out-of-domain. We achieve state-of-the-art results on the MTEB benchmark with no hard negative mining, score distillation, dataset-specific instructions, intra-GPU example-sharing, or extremely large batch sizes. Our method can be applied to improve performance on any contrastive learning dataset and any biencoder.

Contextual Document Embeddings

The paper "Contextual Document Embeddings" introduces a novel approach to improving dense document embeddings for neural retrieval. The traditional paradigm focuses on generating embeddings by encoding individual documents, neglecting the context provided by neighboring documents. This work proposes leveraging contextualized document embeddings, akin to contextualized word embeddings, to enhance retrieval tasks. Two complementary methodologies are presented: a contrastive learning objective incorporating document neighbors into intra-batch contextual loss, and a contextual architecture encoding neighboring document information directly.

Motivation and Methods

The paper challenges the conventional independent encoding approach by emphasizing the potential enhancements achieved through contextual information. Conventional neural models miss out on incorporating prior corpus statistics, unlike statistical models which naturally adapt to varying contexts with terms like inverse document frequency (IDF).

To address this, two primary approaches are proposed:

1. Contextual Contrastive Learning:
   • This involves reformulating the contrastive learning objective to incorporate neighboring documents in the training batches. Such a strategy aims to make batches more challenging, ensuring embeddings can differentiate documents effectively even under domain shifts. The method uses clustering to partition datasets into pseudo-domains, making batch-wise learning more tailored and robust.
2. Contextual Architecture:
   • The architecture leverages a two-stage process for embedding. Initially, contextual documents are embedded using a separate model, and their embeddings are concatenated to construct a contextual sequence. This sequence is then used by the main encoder to integrate critical corpus information during the encoding phase.

Results and Implications

The experiments reveal that both proposed methods outperform standard biencoder architectures across several retrieval tasks, particularly out-of-domain scenarios. Importantly, the contextual approach achieved state-of-the-art results on the MTEB benchmark while omitting traditionally necessary techniques like hard negative mining or large batch sizes.

These findings emphasize the importance of context in neural information retrieval. From a theoretical perspective, the work suggests that integrating corpus-level information directly into document embeddings can significantly bridge the gap neural retrieval models face when stepping into unfamiliar domains.

Future Directions

This research opens pathways for further exploration into context-aware document embeddings. Potential future work could involve extending contextual embedding techniques to other modalities or refining the clustering algorithms for improved pseudo-domain creation. Additionally, exploring the balance between computational efficiency and context depth could yield new insights, especially on scalability.

In conclusion, the methodologies presented highlight a significant advancement in harnessing contextual information, offering an enriched toolkit for researchers and practitioners focusing on domain-adaptive neural retrieval models.