Papers with Dense retrieval models
Augmenting Document Representations for Dense Retrieval with Interpolation and Perturbation (2022.acl-short)
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| Challenge: | Existing sparse retrieval models rely on term-based matching to retrieve relevant documents. |
| Approach: | They propose a framework which augments the representations of documents with interpolation and perturbation. |
| Outcome: | The proposed framework significantly outperforms baselines on the dense retrieval of both the labeled and unlabeled documents. |
Breaking Boundaries in Retrieval Systems: Unsupervised Domain Adaptation with Denoise-Finetuning (2023.findings-emnlp)
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| Challenge: | Existing domain adaptation methods for dense retrieval models use unadapted rerank models, leading to imprecise labels. |
| Approach: | They propose to adapt a rerank model to the target domain before using it for label generation. |
| Outcome: | The proposed model achieves better results across three retrieval datasets. |
The Overlooked Role of Graded Relevance Thresholds in Multilingual Dense Retrieval (2026.findings-acl)
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| Challenge: | Dense retrieval models are fine-tuned with contrastive learning objectives that require binary relevance judgments. |
| Approach: | They examine how graded relevance scores affect multilingual dense retrieval . they argue that a well-chosen threshold can improve effectiveness and mitigate annotation noise . |
| Outcome: | The optimal threshold varies systematically across languages and tasks, the authors show . a well-chosen threshold can improve effectiveness and mitigate annotation noise . |
Collapse of Dense Retrievers: Short, Early, and Literal Biases Outranking Factual Evidence (2025.acl-long)
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| Challenge: | Notably, when multiple biases combine, models exhibit catastrophic performance degradation, selecting the answer-containing document in less than 10% of cases over a synthetic biased document without the answer. |
| Approach: | They repurpose a relation extraction dataset to quantify the impact of heuristic biases on retrievers like Dragon+ and Contriever. |
| Outcome: | The proposed models exhibit catastrophic performance degradation when multiple biases combine, selecting the answer-containing document in less than 10% of cases over a synthetic biased document without the answer. |
Investigating Multi-layer Representations for Dense Passage Retrieval (2025.findings-emnlp)
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| Challenge: | Dense retrieval models adopt vectors from the last hidden layer of the document encoder to represent a document, which is in contrast to the fact that representations in different layers of a pre-trained language model contain different kinds of linguistic knowledge and behave differently during fine-tuning. |
| Approach: | They propose to utilize representations from multiple encoder layers to make up the representation of a document, which they denote Multi-layer Representations (MLR). |
| Outcome: | The proposed model outperforms dual encoder, ME-BERT and ColBERT in the single-vector retrieval setting and with other advanced training techniques. |