Query Drift Compensation: Enabling Compatibility in Continual Learning of Retrieval Embedding Models

• URL: http://arxiv.org/abs/2506.00037v1
• Date: Tue, 27 May 2025 14:52:52 GMT
• Title: Query Drift Compensation: Enabling Compatibility in Continual Learning of Retrieval Embedding Models
• Authors: Dipam Goswami, Liying Wang, Bartłomiej Twardowski, Joost van de Weijer,
• Abstract summary: We study how the already indexed corpus can still be effectively used without the need of re-indexing.<n>We employ embedding distillation on both query and document embeddings to maintain stability.<n>We propose a novel query drift compensation method during retrieval to project new model query embeddings to the old embedding space.
• Score: 12.586519025284328
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Text embedding models enable semantic search, powering several NLP applications like Retrieval Augmented Generation by efficient information retrieval (IR). However, text embedding models are commonly studied in scenarios where the training data is static, thus limiting its applications to dynamic scenarios where new training data emerges over time. IR methods generally encode a huge corpus of documents to low-dimensional embeddings and store them in a database index. During retrieval, a semantic search over the corpus is performed and the document whose embedding is most similar to the query embedding is returned. When updating an embedding model with new training data, using the already indexed corpus is suboptimal due to the non-compatibility issue, since the model which was used to obtain the embeddings of the corpus has changed. While re-indexing of old corpus documents using the updated model enables compatibility, it requires much higher computation and time. Thus, it is critical to study how the already indexed corpus can still be effectively used without the need of re-indexing. In this work, we establish a continual learning benchmark with large-scale datasets and continually train dense retrieval embedding models on query-document pairs from new datasets in each task and observe forgetting on old tasks due to significant drift of embeddings. We employ embedding distillation on both query and document embeddings to maintain stability and propose a novel query drift compensation method during retrieval to project new model query embeddings to the old embedding space. This enables compatibility with previously indexed corpus embeddings extracted using the old model and thus reduces the forgetting. We show that the proposed method significantly improves performance without any re-indexing. Code is available at https://github.com/dipamgoswami/QDC.

Related papers

• SitEmb-v1.5: Improved Context-Aware Dense Retrieval for Semantic Association and Long Story Comprehension [77.93156509994994]
  We show how to represent short chunks in a way that is conditioned on a broader context window to enhance retrieval performance.<n>Existing embedding models are not well-equipped to encode such situated context effectively.<n>Our method substantially outperforms state-of-the-art embedding models.
  arXiv  Detail & Related papers  (2025-08-03T23:59:31Z)
• MURR: Model Updating with Regularized Replay for Searching a Document Stream [32.0637790321157]
  Internet produces a continuous stream of new documents and user-generated queries.<n>Neural retrieval models that were trained once on a fixed set of query-document pairs will quickly start misrepresenting newly-created content.<n>We propose MURR, a model updating strategy with regularized replay, to ensure the model can still faithfully search existing documents without reprocessing.
  arXiv  Detail & Related papers  (2025-04-14T14:13:03Z)
• Quam: Adaptive Retrieval through Query Affinity Modelling [15.3583908068962]
  Building relevance models to rank documents based on user information needs is a central task in information retrieval and the NLP community. We propose a unifying view of the nascent area of adaptive retrieval by proposing, Quam. Our proposed approach, Quam improves the recall performance by up to 26% over the standard re-ranking baselines.
  arXiv  Detail & Related papers  (2024-10-26T22:52:12Z)
• Query-oriented Data Augmentation for Session Search [71.84678750612754]
  We propose query-oriented data augmentation to enrich search logs and empower the modeling. We generate supplemental training pairs by altering the most important part of a search context. We develop several strategies to alter the current query, resulting in new training data with varying degrees of difficulty.
  arXiv  Detail & Related papers  (2024-07-04T08:08:33Z)
• Efficient Document Ranking with Learnable Late Interactions [73.41976017860006]
  Cross-Encoder (CE) and Dual-Encoder (DE) models are two fundamental approaches for query-document relevance in information retrieval. To predict relevance, CE models use joint query-document embeddings, while DE models maintain factorized query and document embeddings. Recently, late-interaction models have been proposed to realize more favorable latency-quality tradeoffs, by using a DE structure followed by a lightweight scorer.
  arXiv  Detail & Related papers  (2024-06-25T22:50:48Z)
• List-aware Reranking-Truncation Joint Model for Search and Retrieval-augmented Generation [80.12531449946655]
  We propose a Reranking-Truncation joint model (GenRT) that can perform the two tasks concurrently. GenRT integrates reranking and truncation via generative paradigm based on encoder-decoder architecture. Our method achieves SOTA performance on both reranking and truncation tasks for web search and retrieval-augmented LLMs.
  arXiv  Detail & Related papers  (2024-02-05T06:52:53Z)
• IncDSI: Incrementally Updatable Document Retrieval [35.5697863674097]
  IncDSI is a method to add documents in real time without retraining the model on the entire dataset. We formulate the addition of documents as a constrained optimization problem that makes minimal changes to the network parameters. Our approach is competitive with re-training the model on the whole dataset.
  arXiv  Detail & Related papers  (2023-07-19T07:20:30Z)
• DSI++: Updating Transformer Memory with New Documents [95.70264288158766]
  We introduce DSI++, a continual learning challenge for DSI to incrementally index new documents. We show that continual indexing of new documents leads to considerable forgetting of previously indexed documents. We introduce a generative memory to sample pseudo-queries for documents and supplement them during continual indexing to prevent forgetting for the retrieval task.
  arXiv  Detail & Related papers  (2022-12-19T18:59:34Z)
• Autoregressive Search Engines: Generating Substrings as Document Identifiers [53.0729058170278]
  Autoregressive language models are emerging as the de-facto standard for generating answers. Previous work has explored ways to partition the search space into hierarchical structures. In this work we propose an alternative that doesn't force any structure in the search space: using all ngrams in a passage as its possible identifiers.
  arXiv  Detail & Related papers  (2022-04-22T10:45:01Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.