DENSE RETRIEVAL

Thesis Proposal: On the Granularity-Robustness Trade-off in Text-Derived Knowledge Graphs (2026.acl-srw)

Challenge:	Existing information retrieval datasets cannot capture abstract semantic associations well.
Approach:	They propose a task that retrieves relevant plots from the book for a query using a labeled dataset.
Outcome:	The proposed task can be used to evaluate the performance of IR models on the novel task Plot Retrieval.

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Surawat Pralomram

RetroMAE: Pre-Training Retrieval-oriented Language Models Via Masked Auto-Encoder (2022.emnlp-main)

Challenge:	Retrieval-augmented generation (RAG) based on dense embeddings is a dominant paradigm for text retrieval, but many real-world applications require attribute-specific querying.
Approach:	They propose a query-driven framework for constructing and retrieving knowledge graphs from text using dense embeddings.
Outcome:	The proposed framework combines the robustness of dense retrieval with the explicit queryability of symbolic representations.

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Shitao Xiao, Zheng Liu, Yingxia Shao, Zhao Cao

Open Domain Question Answering over Tables via Dense Retrieval (2021.naacl-main)

Challenge:	Existing methods for dense retrieval are not effective, but there are still challenges.
Approach:	They propose a retrieval oriented pre-training paradigm based on Masked Auto-Encoder (MAE) where the sentence embedding is generated from the encoder’s masked input and the original sentence is recovered based upon the sentence embedded and decoded input via mangled language modeling.
Outcome:	The proposed model significantly improves the SOTA performance on a wide range of NLP benchmarks, like BEIR and MS MARCO.

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Jonathan Herzig, Thomas Müller, Syrine Krichene, Julian Eisenschlos

Natural Language Interfaces

Challenge:	Recent advances in open-domain QA focus on retrieving textual passages . a retriever designed to handle tabular context can improve retrieval quality .
Approach:	They propose a tabular-based retrieval model that improves retrieval quality over a BERT-based retriever.
Outcome:	The proposed retriever improves retrieval quality with mined hard negatives over a BERT-based retriever.

Evaluating Embedding APIs for Information Retrieval (2023.acl-industry)

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Ehsan Kamalloo, Xinyu Zhang, Odunayo Ogundepo, Nandan Thakur, David Alfonso-hermelo, Mehdi Rezagholizadeh, Jimmy Lin

Operational Advice for Dense and Sparse Retrievers: HNSW, Flat, or Inverted Indexes? (2025.acl-industry)

Challenge:	a growing number of language models are limiting their access to the community . we evaluate existing APIs for domain generalization and multilingual retrieval .
Approach:	They evaluate semantic embedding APIs in retrieval scenarios to assess their capabilities . they use BEIR and MIRACL to re-rank BM25 results using the APIs .
Outcome:	The proposed model is based on semantic embedding APIs that build vector representations of a given text.

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Jimmy Lin

ChatRetriever: Adapting Large Language Models for Generalized and Robust Conversational Dense Retrieval (2024.emnlp-main)

Challenge:	Currently, practitioners working on dense retrieval face a bewildering number of choices.
Approach:	They propose a framework for thinking about retrieval in terms of nearest-neighbor search over vector representations where these representations can be dense (typically called embeddings, generated from transformers) or flat (with brute-force search)
Outcome:	The proposed model explicates tradeoffs between HNSW and flat indexes from the perspectives of indexing time, query evaluation performance, and retrieval quality.

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Kelong Mao, Chenlong Deng, Haonan Chen, Fengran Mo, Zheng Liu, Tetsuya Sakai, Zhicheng Dou

Large Language Models as Foundations for Next-Gen Dense Retrieval: A Comprehensive Empirical Assessment (2024.emnlp-main)

Challenge:	a conversational search system requires accurate interpretation of user intent from complex multi-turn contexts.
Approach:	They propose a dual-learning approach that adapts LLMs for retrieval via contrastive learning while enhancing the complex session understanding through masked instruction tuning.
Outcome:	The proposed approach outperforms existing retrieval methods on five conversational search benchmarks.

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Kun Luo, Minghao Qin, Zheng Liu, Shitao Xiao, Jun Zhao, Kang Liu

Decoding Dark Matter: Specialized Sparse Autoencoders for Interpreting Rare Concepts in Foundation Models (2025.findings-naacl)

Challenge:	Pre-trained language models have limited generalization capabilities and performance challenges.
Approach:	They evaluate 15 different backbone LLMs and non-LLMs to evaluate their performance . larger models and extensive pre-training consistently enhance in-domain accuracy and data efficiency .
Outcome:	The results show that larger models and extensive pre-training enhance in-domain accuracy and data efficiency.

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Aashiq Muhamed, Mona T. Diab, Virginia Smith

COCO-DR: Combating Distribution Shift in Zero-Shot Dense Retrieval with Contrastive and Distributionally Robust Learning (2022.emnlp-main)

Challenge:	Sparse Autoencoders (SAEs) are a promising tool for disentangling FM representations, but they struggle to capture rare, yet crucial concepts in the data.
Approach:	They propose a technique to train Sparse Autoencoders to illuminate elusive dark matter features by focusing on specific subdomains.
Outcome:	The proposed method achieves 12.5% better classification accuracy than general-purpose SAEs when applied to remove spurious gender information.

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Yue Yu, Chenyan Xiong, Si Sun, Chao Zhang, Arnold Overwijk

Typo-Robust Representation Learning for Dense Retrieval (2023.acl-short)

Challenge:	Using COCO-DR, we combat distribution shifts between source training tasks and target scenarios.
Approach:	They propose a method to combat distribution shifts between source training tasks and target scenarios by COtinuous COtrastive learning.
Outcome:	The proposed method outperforms existing models on BEIR and the giant GPT-3 embedding model with 500x more parameters.

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Panuthep Tasawong, Wuttikorn Ponwitayarat, Peerat Limkonchotiwat, Can Udomcharoenchaikit, Ekapol Chuangsuwanich, Sarana Nutanong

Representation Learning

Challenge:	Dense retrieval is a fundamental building block of information retrieval applications.
Approach:	They propose a method that aligns misspelled queries with their pristine counterparts to improve contrast between each query and its surrounding queries.
Outcome:	The proposed method outperforms the competitors in all cases with misspelled queries.

Precise Zero-Shot Dense Retrieval without Relevance Labels (2023.acl-long)

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Luyu Gao, Xueguang Ma, Jimmy Lin, Jamie Callan

DPTDR: Deep Prompt Tuning for Dense Passage Retrieval (2022.coling-1)

Challenge:	Existing dense retrieval systems that use semantic embedding similarities can be effective across tasks and languages.
Approach:	They propose to pivot through Hypothetical Document Embeddings (HyDE) given a query, HyDE first zero-shot prompts an instruction-following language model to generate a hypothetical document.
Outcome:	The proposed method significantly outperforms the state-of-the-art unsupervised dense retriever Contriever and shows strong performance comparable to fine-tuned retrievers across tasks and languages.

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Zhengyang Tang, Benyou Wang, Ting Yao

Hybrid Inverted Index Is a Robust Accelerator for Dense Retrieval (2023.emnlp-main)

Challenge:	Recent studies show that prompt tuning is unfriendly for industrial deployment in dense retrieval tasks.
Approach:	They propose to apply prompt tuning to dense retrieval tasks to reduce deployment cost . they propose to use retrieval-oriented intermediate pretraining and unified negative mining .
Outcome:	The proposed method outperforms state-of-the-art models on MS-MARCO and Natural Questions.

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Peitian Zhang, Zheng Liu, Shitao Xiao, Zhicheng Dou, Jing Yao

TegTok: Augmenting Text Generation via Task-specific and Open-world Knowledge (2022.findings-acl)

Challenge:	Inverted file structure is a common technique for accelerating dense retrieval, but its lossy nature degrades it.
Approach:	They propose a hybrid index where embedding clusters and salient terms work collaboratively to accelerate dense retrieval.
Outcome:	The proposed method achieves lossless retrieval quality with competitive efficiency across index settings.

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Chao-Hong Tan, Jia-Chen Gu, Chongyang Tao, Zhen-Hua Ling, Can Xu, Huang Hu, Xiubo Geng, Daxin Jiang

M3-Embedding: Multi-Linguality, Multi-Functionality, Multi-Granularity Text Embeddings Through Self-Knowledge Distillation (2024.findings-acl)

Challenge:	Generating natural and informative texts has been a long-standing problem in NLP.
Approach:	They propose to augment TExt Generation via Task-specific and Open-world Knowledge in a unified framework.
Outcome:	The proposed model can learn what and how to generate on two text generation tasks.

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Jianlyu Chen, Shitao Xiao, Peitian Zhang, Kun Luo, Defu Lian, Zheng Liu

What Are You Token About? Dense Retrieval as Distributions Over the Vocabulary (2023.acl-long)

Challenge:	In this paper, we introduce a new embedding model for semantic retrieval of more than 100 working languages.
Approach:	They propose a new embedding model that supports multi-lingual, cross-lingual and long-document retrieval . they propose integrating relevance scores from different retrieval functionalities into the teacher signal .
Outcome:	The proposed model exhibits superior performance on multilingual, cross-lingual, and long-document retrieval benchmarks.

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Ori Ram, Liat Bezalel, Adi Zicher, Yonatan Belinkov, Jonathan Berant, Amir Globerson

Logical Form Generation via Multi-task Learning for Complex Question Answering over Knowledge Bases (2022.coling-1)

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.
Outcome:	The proposed model significantly improves on the BEIR benchmark and in zero-shot settings.

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Xixin Hu, Xuan Wu, Yiheng Shu, Yuzhong Qu

Negative Sampling Techniques in Dense Retrieval: A Survey (2026.findings-eacl)

Challenge:	Existing generation-based KBQA methods that translate natural language questions to executable logical forms are proving promising but noise introduced can lead to incorrect results.
Approach:	They propose a Generation-based KBQA method that uses auxiliary information to enhance logical form generation by combining unseen KB items with novel combinations.
Outcome:	The proposed method achieves state-of-the-art results on ComplexWebQuestions and WebQuestIONSSP datasets.

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Laurin Wischounig, Abdelrahman Abdallah, Adam Jatowt

Llama2Vec: Unsupervised Adaptation of Large Language Models for Dense Retrieval (2024.acl-long)

Challenge:	Information Retrieval (IR) is fundamental to many modern NLP applications.
Approach:	They propose a taxonomy that categorizes negative sampling techniques in dense IR . they analyze them with respect to trade-offs between effectiveness, computational cost, implementation difficulty .
Outcome:	The proposed taxonomy categorizes techniques using random, static/dynamically mined, and synthetic datasets.

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Chaofan Li, Zheng Liu, Shitao Xiao, Yingxia Shao, Defu Lian

Finding the Law: Enhancing Statutory Article Retrieval via Graph Neural Networks (2023.eacl-main)

Challenge:	Dense retrieval requires discriminative embeddings to represent the semantic relationship between query and document.
Approach:	They propose an unsupervised approach that performs unsupervised adaptation of large language models for dense retrieval.
Outcome:	The proposed model improves on a variety of dense retrieval benchmarks and is available on github.

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Antoine Louis, Gijs van Dijck, Gerasimos Spanakis

Unsupervised Corpus Aware Language Model Pre-training for Dense Passage Retrieval (2022.acl-long)

Challenge:	Statutory article retrieval (SAR) is a promising application of legal text processing.
Approach:	They propose a graph-augmented dense statute retriever model that incorporates the structure of legislation via a neural network to improve density retrieval performance.
Outcome:	The proposed model outperforms baselines on a real-world expert-annotated dataset.

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Luyu Gao, Jamie Callan

CORE: A Retrieve-then-Edit Framework for Counterfactual Data Generation (2022.findings-emnlp)

Challenge:	Recent research shows that fine-tuning dense retrievers to realize their capacity requires carefully designed fine-cuning techniques.
Approach:	They propose a pre-training architecture that learns to condense information into the dense vector through LM pre-training and a coCondenser architecture which adds an unsupervised corpus-level contrastive loss to warm up the passage embedding space.
Outcome:	The proposed architecture reduces the need for heavy data engineering and large batch training.

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Tanay Dixit, Bhargavi Paranjape, Hannaneh Hajishirzi, Luke Zettlemoyer

Less is More: Pretrain a Strong Siamese Encoder for Dense Text Retrieval Using a Weak Decoder (2021.emnlp-main)

Challenge:	Prior work on counterfactual data augmentation only considered restricted classes of perturbations, limiting their effectiveness.
Approach:	They propose a retrieval-augmented framework for creating diverse counterfactual perturbations for CDA.
Outcome:	Experiments on natural language inference and sentiment analysis show that the proposed framework can be used to encourage diversity in manually authored perturbations.

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Shuqi Lu, Di He, Chenyan Xiong, Guolin Ke, Waleed Malik, Zhicheng Dou, Paul Bennett, Tie-Yan Liu, Arnold Overwijk

Longtriever: a Pre-trained Long Text Encoder for Dense Document Retrieval (2023.emnlp-main)

Challenge:	Dense retrieval requires high-quality text sequence embeddings to support effective search in the representation space.
Approach:	They propose a self-learning method that pre-trains the autoencoder using a weak decoder to push the encoder to provide better sequence representations.
Outcome:	The proposed model significantly boosts the effectiveness and few-shot ability of dense retrieval models on web search, news recommendation, and open domain question answering.

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Junhan Yang, Zheng Liu, Chaozhuo Li, Guangzhong Sun, Xing Xie

Bridging the Training-Inference Gap for Dense Phrase Retrieval (2022.findings-emnlp)

Challenge:	Existing PLMs are infeasible for processing long documents due to computational costs and incomprehensive document understanding.
Approach:	They propose a retrieval model that models local semantics and global context semantics in a tightly-coupled manner.
Outcome:	The proposed model overcomes three core challenges of long document retrieval: substantial computational cost, incomprehensive document understanding, and scarce annotations.

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Gyuwan Kim, Jinhyuk Lee, Barlas Oguz, Wenhan Xiong, Yizhe Zhang, Yashar Mehdad, William Yang Wang

LaPraDoR: Unsupervised Pretrained Dense Retriever for Zero-Shot Text Retrieval (2022.findings-acl)

Challenge:	Existing methods for building dense retrievers are often misaligned and do not reflect retrieval scenario at inference time.
Approach:	They propose a way to validate dense retrievers using a small subset of the entire corpus.
Outcome:	The proposed model improves top-1 phrase retrieval accuracy by 2 3 points and top-20 passage retrieval by 2 4 points for open-domain question answering.

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Canwen Xu, Daya Guo, Nan Duan, Julian McAuley

UniConv: Unifying Retrieval and Response Generation for Large Language Models in Conversations (2025.acl-long)

Challenge:	Experimental results show that LaPraDoR is state-of-the-art compared with supervised dense retrieval models.
Approach:	They propose a pretrained dual-tower dense retriever that does not require supervised data for training.
Outcome:	The proposed method achieves state-of-the-art performance on 18 datasets of 9 zero-shot text retrieval tasks.

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Fengran Mo, Yifan Gao, Chuan Meng, Xin Liu, Zhuofeng Wu, Kelong Mao, Zhengyang Wang, Pei Chen, Zheng Li, Xian Li, Bing Yin, Meng Jiang

Dialogue Systems & Conversational Agents

Challenge:	Existing conversational search systems are usually built with two different models . this separation restricts the system from leveraging the model's intrinsic knowledge simultaneously . Existing studies for developing unified models cannot fully address the aspects of understanding conversational context, managing retrieval independently, and generating responses.
Approach:	They propose to unify dense retrieval and response generation for large language models in conversation by fine-tuning and mitigating data discrepancy.
Outcome:	The proposed model can outperform existing models on five conversational search datasets and reduce inconsistency risks while mitigating data discrepancy.

Optimizing Test-Time Query Representations for Dense Retrieval (2023.findings-acl)

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Mujeen Sung, Jungsoo Park, Jaewoo Kang, Danqi Chen, Jinhyuk Lee

BERM: Training the Balanced and Extractable Representation for Matching to Improve Generalization Ability of Dense Retrieval (2023.acl-long)

Challenge:	Recent developments of dense retrieval rely on quality representations of queries and contexts from pre-trained query and context encoders.
Approach:	They propose a test-time optimization of query representations that provides fine-grained pseudo labels over retrieval results.
Outcome:	The proposed algorithm improves open-domain question answering accuracy and direct re-ranking by up to 2.0% while running 1.3–2.4x faster with an efficient implementation.

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Shicheng Xu, Liang Pang, Huawei Shen, Xueqi Cheng

The Overlooked Role of Graded Relevance Thresholds in Multilingual Dense Retrieval (2026.findings-acl)

Challenge:	Dense retrieval has shown promise in the first-stage retrieval process when trained on in-domain labeled datasets.
Approach:	They propose a method to capture matching signal to improve generalization of dense retrieval by capturing matching signal between two texts.
Outcome:	The proposed method can be combined with different training methods to improve generalization ability without additional inference overhead and target domain data.

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Tomer Wullach, Ori Shapira, Amir David Nissan Cohen

Multilinguality

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 .

Cross-lingual Contextualized Phrase Retrieval (2024.findings-emnlp)

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Huayang Li, Deng Cai, Zhi Qu, Qu Cui, Hidetaka Kamigaito, Lemao Liu, Taro Watanabe

Multilinguality

Challenge:	Phrase-level dense retrieval has shown many appealing characteristics in downstream NLP tasks.
Approach:	They propose a task formulation of dense retrieval, cross-lingual contextualized phrase retrieval . they extract pairs of cross-linguistic phrases using word alignment information .
Outcome:	The proposed task formulation surpasses baselines on the phrase retrieval task and a downstream task, i.e., machine translation, and achieves top-1 accuracy 13 points higher.

Dimension Reduction for Efficient Dense Retrieval via Conditional Autoencoder (2022.emnlp-main)

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Zhenghao Liu, Han Zhang, Chenyan Xiong, Zhiyuan Liu, Yu Gu, Xiaohua Li

Taxonomy-guided Semantic Indexing for Academic Paper Search (2024.emnlp-main)

Challenge:	Existing work reserves the principle dimensions of query and document embeddings for building more efficient retrieval systems.
Approach:	They propose to use Conditional Autoencoder to compress high-dimensional embeddings to maintain the same embeddable distribution and better recover ranking features.
Outcome:	The proposed algorithm achieves comparable ranking performance with its teacher model and makes the retrieval system more efficient.

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SeongKu Kang, Yunyi Zhang, Pengcheng Jiang, Dongha Lee, Jiawei Han, Hwanjo Yu

How to Train Your Dragon: Diverse Augmentation Towards Generalizable Dense Retrieval (2023.findings-emnlp)

Challenge:	Academic paper search often struggles to match underlying academic concepts between queries and documents.
Approach:	They propose a framework that extracts key concepts from papers and organizes them as a semantic index guided by an academic taxonomy.
Outcome:	The proposed framework can be flexibly employed to enhance existing retrieval frameworks.

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Sheng-Chieh Lin, Akari Asai, Minghan Li, Barlas Oguz, Jimmy Lin, Yashar Mehdad, Wen-tau Yih, Xilun Chen

Domain Adaptation for Dense Retrieval and Conversational Dense Retrieval through Self-Supervision by Meticulous Pseudo-Relevance Labeling (2024.lrec-main)

Challenge:	Existing techniques to improve dense retrieval suffer from effectiveness tradeoffs between supervised and zero-shot retrieval, some argue due to the limited model capacity.
Approach:	They propose to use diverse queries and sources of supervision to train a generalizable DR to achieve high accuracy in both supervised and zero-shot retrieval.
Outcome:	The proposed DR can achieve state-of-the-art in supervised and zero-shot evaluations without increasing model size.

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Minghan Li, Eric Gaussier

Low-Resource NLP

Challenge:	Recent studies have shown that dense retrieval models generalize less well than interaction-based models on out-of-distribution data sets.
Approach:	They propose to combine query-generation approach with self-supervision approach in which pseudo-relevance labels are automatically generated on the target domain.
Outcome:	The proposed approach is based on a T5-3B model for pseudo-positive labeling and hard negatives on conversational dense retrieval models.

Topic-DPR: Topic-based Prompts for Dense Passage Retrieval (2023.findings-emnlp)

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Qingfa Xiao, Shuangyin Li, Lei Chen

Low-Resource NLP

Challenge:	Prior research focused on optimizing a single prompt as a continuous prompt, but this approach leads to a semantic space collapse, preventing differentiation between relevant and irrelevant passages.
Approach:	They propose a dense passage retrieval model that uses topic-based prompts and propose 'positive and negative sampling strategies' to boost dense retrieval efficiency.
Outcome:	The proposed model surpasses state-of-the-art retrieval techniques and improves space uniformity.

Polar Ducks and Where to Find Them: Enhancing Entity Linking with Duck Typing and Polar Box Embeddings (2023.emnlp-main)

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Mattia Atzeni, Mikhail Plekhanov, Frederic Dreyer, Nora Kassner, Simone Merello, Louis Martin, Nicola Cancedda

Making Large Language Models Efficient Dense Retrievers (2026.acl-long)

Challenge:	Entity linking methods based on dense retrieval are often not efficient in large-scale applications as they are sensitive to the structure of the embedding space.
Approach:	They propose a method to infuse structural information into the space of entity representations by using prior knowledge of entity types.
Outcome:	The proposed method outperforms other type-aware approaches and matches generative models with 18 times more parameters.

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Yibin Lei, Shwai He, Ang Li, Andrew Yates

AfriCLIRMatrix: Enabling Cross-Lingual Information Retrieval for African Languages (2022.emnlp-main)

Challenge:	Recent studies have shown that fine-tuning large language models for dense retrieval yields strong performance, but their substantial parameter counts make them computationally inefficient.
Approach:	They propose a framework for developing efficient retrievers that performs coarse-to-fine compression through a coarse-grained coarse-tuning strategy.
Outcome:	The proposed framework reduces model size and inference cost while preserving performance of full-size models.

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Odunayo Ogundepo, Xinyu Zhang, Shuo Sun, Kevin Duh, Jimmy Lin

Multilinguality

Challenge:	Existing datasets for cross-lingual information retrieval are limited in many languages, especially those spoken in Africa.
Approach:	They propose to build a test collection for cross-lingual information retrieval in 15 diverse African languages.
Outcome:	AfriCLIRMatrix contains 6 million queries in English and 23 million relevance judgments automatically mined from Wikipedia inter-language links, covering many more African languages than any existing information retrieval test collection.

Boosting Data Utilization for Multilingual Dense Retrieval (2025.emnlp-main)

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Chao Huang, Fengran Mo, Yufeng Chen, Changhao Guan, Zhenrui Yue, Xinyu Wang, Jinan Xu, Kaiyu Huang

CAPSTONE: Curriculum Sampling for Dense Retrieval with Document Expansion (2023.emnlp-main)

Challenge:	Existing studies focus on fine-tuning multilingual dense retrieval models, but data scarcity for low-resource languages makes it difficult to align representations in a shared vector space.
Approach:	They propose to obtain high-quality hard negative samples and effective mini-batch data to boost data utilization for multilingual dense retrieval by obtaining high-quality negative samples.
Outcome:	The proposed method outperforms existing baselines on a multilingual retrieval benchmark, MIRACL, with 16 languages.

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Xingwei He, Yeyun Gong, A-Long Jin, Hang Zhang, Anlei Dong, Jian Jiao, Siu Yiu, Nan Duan

PQR: Improving Dense Retrieval via Potential Query Modeling (2025.acl-long)

Challenge:	Experimental results show that dense retrieval models are better at obtaining query-informed representations.
Approach:	They propose a dual-encoder approach that computes latent representations of query and document independently, but inference replaces the real query with a generated one.
Outcome:	The proposed approach outperforms previous dense retrieval models on in-domain and out-of-domain datasets.

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Junfeng Kang, Rui Li, Qi Liu, Yanjiang Chen, Zheng Zhang, Junzhe Jiang, Heng Yu, Yu Su

mAggretriever: A Simple yet Effective Approach to Zero-Shot Multilingual Dense Retrieval (2023.emnlp-main)

Challenge:	Existing training data is sparse, with each document associated with one or a few labeled queries.
Approach:	They propose a training-free potential query retrieval framework to address this problem . they use a Gaussian mixture distribution to model all potential queries for a document .
Outcome:	The proposed method is able to capture comprehensive semantic information from a document with multiple queries.

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Sheng-Chieh Lin, Amin Ahmad, Jimmy Lin

Knowing When Not to Answer: Lightweight KB-Aligned OOD Detection for Safe RAG (2026.acl-long)

Challenge:	MLIR requires human annotations in multiple languages, making training labor-intensive.
Approach:	They propose a multilingual information retrieval model that leverages pre-trained multilingual transformers for dense retrieval.
Outcome:	Empirical results show that mAggretriever outperforms state-of-the-art models fine-tuned on English training data.

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Ilias Triantafyllopoulos, Renyi Qu, Salvatore Giorgi, Brenda Curtis, Lyle Ungar, João Sedoc

Noisy Pair Corrector for Dense Retrieval (2023.findings-emnlp)

Challenge:	Retrieval-Augmented Generation (RAG) systems are increasingly deployed in high-stakes domains where safety depends on how a system answers . out-of-domain (OOD) queries can impair performance and safety .
Approach:	They propose to use lightweight, KB-aligned OOD detection as an always-on gate for RAG systems.
Outcome:	The proposed method scores queries in a compact subspace selected either by explained-variance retention (EVR) or by a separability-driven -test ranking.

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Hang Zhang, Yeyun Gong, Xingwei He, Dayiheng Liu, Daya Guo, Jiancheng Lv, Jian Guo

Dense Passage Retrieval: Is it Retrieving? (2024.findings-emnlp)

Challenge:	Existing dense retrieval models assume that query-document pairs are exactly matched, resulting in mismatched-pair noise.
Approach:	They propose a novel approach to train an effective model with mismatched-pair noise.
Outcome:	The proposed model performs well on natural question and triviaQA, code-search benchmarks and SO-DS.

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Benjamin Reichman, Larry Heck

Dense X Retrieval: What Retrieval Granularity Should We Use? (2024.emnlp-main)

Challenge:	Large Language Models (LLMs) internally store repositories of knowledge, but access to these repositoriels is imprecise.
Approach:	They propose a paradigm called retrieval augmented generation to address hallucinations . they analyze the role of fine-tuning pre-trained networks to enhance alignment .
Outcome:	The proposed paradigm addresses hallucinations by fine-tuning pre-trained models . the model can be decentralized, inject facts as decentralized representations .

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Tong Chen, Hongwei Wang, Sihao Chen, Wenhao Yu, Kaixin Ma, Xinran Zhao, Hongming Zhang, Dong Yu

M3: A Multi-Task Mixed-Objective Learning Framework for Open-Domain Multi-Hop Dense Sentence Retrieval (2024.lrec-main)

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 .
Outcome:	The proposed retrieval unit outperforms passage-level units on retrieval and downstream tasks.

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Yang Bai, Anthony Colas, Christan Grant, Zhe Wang

ExpandR: Teaching Dense Retrievers Beyond Queries with LLM Guidance (2025.emnlp-main)

Challenge:	Recent research shows that contrastive learning can lead to suboptimal retrieval performance.
Approach:	They propose an advanced recursive Multi-hop dense sentence retrieval system built upon a novel Multi-task Mixed-objective approach for dense text representation learning.
Outcome:	The proposed approach yields state-of-the-art performance on a large-scale open-domain fact verification benchmark dataset, FEVER.

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Sijia Yao, Pengcheng Huang, Zhenghao Liu, Yu Gu, Yukun Yan, Shi Yu, Ge Yu

Dense Retrieval as Indirect Supervision for Large-space Decision Making (2023.findings-emnlp)

Challenge:	Existing methods for enhancing dense retrieval with query augmentation ignore the alignment between generation and ranking objectives.
Approach:	They propose a unified LLM-augmented dense retrieval framework that jointly optimizes both the LLM and the retriever.
Outcome:	Experimental results show that ExpandR outperforms strong baselines, achieving more than 5% improvement in retrieval performance.

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Nan Xu, Fei Wang, Mingtao Dong, Muhao Chen

PairDistill: Pairwise Relevance Distillation for Dense Retrieval (2024.emnlp-main)

Challenge:	Dense Decision Retrieval (DDR) is a learning-to-retrieve task for discriminative natural language understanding (NLU) tasks with large label spaces.
Approach:	They propose a novel approach to learning large-space discriminative NLU tasks as a learning-to-retrieve task by adopting a dual-encoder architecture that learns to predict by retrieving from a decision thesaurus.
Outcome:	The proposed approach outperforms baselines greatly on multi-label classification tasks, 1.17% in F1 score ultra-fine entity typing, and 1.26% in accuracy on three few-shot intent classification tasks on average.

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Chao-Wei Huang, Yun-Nung Chen

A Thorough Examination on Zero-shot Dense Retrieval (2023.findings-emnlp)

Challenge:	Recent advances in dense retrieval have demonstrated remarkable efficacy compared to traditional sparse retrieval methods.
Approach:	They propose to use pairwise relevance distillation to leverage pairwise reranking to enrich the training of dense retrieval models.
Outcome:	The proposed method outperforms existing methods and achieves state-of-the-art results on multiple benchmarks.

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Ruiyang Ren, Yingqi Qu, Jing Liu, Xin Zhao, Qifei Wu, Yuchen Ding, Hua Wu, Haifeng Wang, Ji-Rong Wen

RADCoT: Retrieval-Augmented Distillation to Specialization Models for Generating Chain-of-Thoughts in Query Expansion (2024.lrec-main)

Challenge:	Recent advances in dense retrieval (DR) models have been shown to be not as competitive as traditional sparse retrieval models in a zero-shot retrieval setting.
Approach:	They propose to examine the zero-shot capability of DR models by analyzing key factors related to source training set and potential bias from target dataset.
Outcome:	The proposed model is not as competitive as sparse retrieval models in a zero-shot retrieval setting.

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Sung-Min Lee, Eunhwan Park, DongHyeon Jeon, Inho Kang, Seung-Hoon Na

Dense Retrieval with Quantity Comparison Intent (2025.findings-acl)

Challenge:	Large language models (LLMs) have demonstrated superior performance to that of small language models in information retrieval for various subtasks including dense retrieval, reranking, query expansion, and pseudo-document generation.
Approach:	They propose a retrieval-augmented model specialization that distills the capability of LLMs to generate the chain-of-thoughts (CoT) for query expansion into a RADCoT.
Outcome:	The proposed model can generate the chain-of-thoughts (CoT) for query expansion, reducing the burden of internalizing and retaining world knowledge in model parameters.

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Prayas Agrawal, Nandeesh Kumar K M, Muthusamy Chelliah, Surender Kumar, Soumen Chakrabarti

Leveraging Cognitive Complexity of Texts for Contextualization in Dense Retrieval (2025.emnlp-main)

Challenge:	Existing sparse and dense retrieval systems fragment numerals and units that express quantities in arbitrary ways.
Approach:	They propose a dense retrieval system built around a density multi-vector index . they propose eliciting and exploiting quantities and associated comparison intents .
Outcome:	The proposed system is faster and more accurate than popular PLMs on two public and one proprietary e-commerce benchmarks.

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Effrosyni Sokli, Georgios Peikos, Pranav Kasela, Gabriella Pasi

Let Retrievers Think Before Action: Thought-Augmented Embedding for Dense Retrieval (2026.findings-acl)

Challenge:	Existing approaches to estimate semantic similarity of queries and documents rely on token-level information derived from query/document interactions.
Approach:	They propose a new DRM that leverages query/document interactions based on full embedding representations generated by a Transformer-based model.
Outcome:	The proposed model outperforms fine-tuning techniques on lightweight bi-encoders and traditional late-interaction models.

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Ruiran Yan, Wen Xiong, Ze Liu, Chaozhuo Li, Hao Liao, Defu Lian, Zheng Liu

Uncovering the Bigger Picture: Comprehensive Event Understanding Via Diverse News Retrieval (2025.emnlp-main)

Challenge:	Large language models have demonstrated that explicit step-by-step thinking can substantially improve performance on complex tasks.
Approach:	They propose a model that generates preliminary thoughts for input queries before document retrieval.
Outcome:	The proposed model generates preliminary thoughts for input queries before document retrieval.

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Yixuan Tang, Yuanyuan Shi, Yiqun Sun, Anthony Kum Hoe Tung

Text Clustering

Challenge:	Existing news retrieval systems prioritize textual relevance, leading to redundancy and limited viewpoint exposure.
Approach:	They propose a framework that explicitly models semantic variation at the sentence level.
Outcome:	The proposed framework outperforms strong baselines in terms of retrieval diversity without compromising relevance.

SURE or Not? Investigating Semantic Understanding in Dense Retrieval Models (2026.acl-long)

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Lingdi Kong, Xuanang Chen, Ben He, Le Sun