Challenge: a recent study has shown that dense retrieval methods are suboptimal for capturing contextual similarities in complex data.
Approach: They propose to combine a structure search method and efficient bi-encoder dense retrieval models to capture contextual similarities.
Outcome: The proposed model improves on token-level and passage-level dense retrieval tasks.

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MaRF: Leveraging Representation-Level Fusion of Formula Semantics for Mathematical Information Retrieval (2026.findings-acl)

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Challenge: Mathematical information retrieval (MIR) relies on combining textual content with mathematical expressions.
Approach: They propose a dual-encoder representation-level fusion framework for MIR that integrates formula semantics into context-aware dense retrieval.
Outcome: The proposed framework outperforms baselines on the ARQMath-3 benchmark.
Dense Passage Retrieval for Open-Domain Question Answering (2020.emnlp-main)

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Challenge: Open-domain question answering relies on efficient passage retrieval to select candidate contexts.
Approach: They propose a dual-encoder framework that can be implemented to retrieve passages from a small number of questions and passages.
Outcome: The proposed system outperforms a strong Lucene-BM25 system in top-20 passage retrieval accuracy on multiple open-domain QA benchmarks.
PAIR: Leveraging Passage-Centric Similarity Relation for Improving Dense Passage Retrieval (2021.findings-acl)

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Challenge: Recent studies only consider query-centric similarity relation when learning the dual-encoder retriever.
Approach: They propose a query-centric and PAssage-centric approach to capture more comprehensive similarity relations for dense passage retrieval.
Outcome: The proposed approach significantly outperforms existing models on both MSMARCO and Natural Questions datasets.
Dense X Retrieval: What Retrieval Granularity Should We Use? (2024.emnlp-main)

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Challenge: a learned dense retrieval model is often overlooked when using a corpus for inference, resulting in a design choice of retrieval unit . granularity of retrievals is important for both retrieval and downstream tasks .
Approach: They propose a retrieval unit for dense retrieval that uses propositions to index corpus . propositions are defined as atomic expressions within text, each encapsulating a distinct factoid .
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Dense Hierarchical Retrieval for Open-domain Question Answering (2021.findings-emnlp)

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Challenge: Dense neural text retrieval has achieved promising results on open-domain Question Answering (QA) current dense retrievers require splitting documents into short passages that usually contain local, partial and sometimes biased context, and may yield inaccurate and misleading hidden representations, thus deteriorating the final retrieval result.
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Outcome: The proposed framework significantly outperforms the original dense passage retriever and helps an end-to-end QA system outperfect the strong baselines on multiple open-domain QA benchmarks.
GNN-encoder: Learning a Dual-encoder Architecture via Graph Neural Networks for Dense Passage Retrieval (2022.findings-emnlp)

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Challenge: Existing approaches to perform large-scale query-passage retrieval are term-based, but they lose interaction between query-pastage pairs.
Approach: They propose to fuse query (passage) information into query representations via graph neural networks that are constructed by queries and their top retrieved passages.
Outcome: The proposed model outperforms existing models on MSMARCO, Natural Questions and TriviaQA datasets and achieves the new state-of-the-art on these datasets.
Sparse, Dense, and Attentional Representations for Text Retrieval (2021.tacl-1)

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Challenge: Dual encoders perform retrieval by encoding documents and queries into dense low-dimensional vectors, scoring each document by its inner product with the query.
Approach: They propose a dual-encoder-based neural model that combines the efficiency of dual encoders with expressiveness of more costly attentional architectures.
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What Are You Token About? Dense Retrieval as Distributions Over the Vocabulary (2023.acl-long)

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Challenge: Dense retrieval models based on text representations have proven very effective, but when applied off-the-shelf they often experience a severe drop in performance.
Approach: They propose to interpret the vector representations produced by dual encoders by projecting them into the model’s vocabulary space.
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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.
Phrase Retrieval Learns Passage Retrieval, Too (2021.emnlp-main)

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Challenge: Dense retrieval methods have shown great promise over sparse methods in a range of NLP problems.
Approach: They propose to use dense phrase retrieval to learn coarse-level retrieval including passages . they show phrase retrievals can be fine-tuned for more coarse-grained retrieval units .
Outcome: The proposed method improves passage retrieval accuracy and QA performance with fewer passages.

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