| Challenge: | Templated answers are used to cover a wide range of topics, but the number of templates is often too high for an agent to manually search. |
| Approach: | They propose a dense retrieval framework that adapts a standard in-batch negatives technique to support unpaired sampling of queries and templates. |
| Outcome: | The proposed approach improves performance and training speed over more standard methods. |
Similar Papers
Boosting Data Utilization for Multilingual Dense Retrieval (2025.emnlp-main)
Copied to clipboard
| 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. |
Negative Sampling Techniques in Dense Retrieval: A Survey (2026.findings-eacl)
Copied to clipboard
| 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. |
Bridging the Training-Inference Gap for Dense Phrase Retrieval (2022.findings-emnlp)
Copied to clipboard
| 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. |
Unsupervised Dense Retrieval with Relevance-Aware Contrastive Pre-Training (2023.findings-acl)
Copied to clipboard
| Challenge: | Dense retrievers have impressive performance, but their demand for abundant training data limits their application scenarios. |
| Approach: | They propose a method which uses unlabeled data to construct pseudo-positive examples from unlabelled data and then contrastively weighs the contrastive loss of different pairs according to the estimated relevance. |
| Outcome: | The proposed method beats the SOTA unsupervised Contriever model on BEIR and open-domain QA retrieval benchmarks and is a good few-shot learner. |
Query-as-context Pre-training for Dense Passage Retrieval (2023.emnlp-main)
Copied to clipboard
| Challenge: | Existing methods to improve passage retrieval performance by using context-supervised pre-training are weakly correlated. |
| Approach: | They propose to use query-as-context pre-training to train passage-query pairs . they evaluate the pre-trained models on large-scale passage retrieval benchmarks . |
| Outcome: | The proposed technique improves performance on large-scale passage retrieval benchmarks and out-of-domain zero-shot benchmarks. |
SimANS: Simple Ambiguous Negatives Sampling for Dense Text Retrieval (2022.emnlp-industry)
Copied to clipboard
Kun Zhou, Yeyun Gong, Xiao Liu, Wayne Xin Zhao, Yelong Shen, Anlei Dong, Jingwen Lu, Rangan Majumder, Ji-rong Wen, Nan Duan
| Challenge: | Existing methods for sapping negatives from large document pool suffer from the uninformative or false negative problem. |
| Approach: | They propose a method to sample negatives from a large document pool using a new sampling probability distribution. |
| Outcome: | The proposed method can be used to sample more ambiguous negatives on four public and one industry datasets. |
Typo-Robust Representation Learning for Dense Retrieval (2023.acl-short)
Copied to clipboard
Panuthep Tasawong, Wuttikorn Ponwitayarat, Peerat Limkonchotiwat, Can Udomcharoenchaikit, Ekapol Chuangsuwanich, Sarana Nutanong
| 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. |
Generative Dense Retrieval: Memory Can Be a Burden (2024.eacl-long)
Copied to clipboard
| Challenge: | Empirical results show that Generative Dense Retrieval (GDR) achieves an average of 3.0 R@100 improvement on NQ dataset under multiple settings and has better scalability. |
| Approach: | They propose a Generative Dense Retrieval paradigm that auto-decodes document identifiers given a query and uses memory to avoid memory confusion. |
| Outcome: | Empirical results show that the proposed paradigm improves on the small-scale corpora and improves scalability. |
On Synthetic Data Strategies for Domain-Specific Generative Retrieval (2025.acl-long)
Copied to clipboard
| Challenge: | Generative retrieval models can be used to generate ranked lists of potentially relevant document identifiers for a user query. |
| Approach: | They propose a synthetic data generation strategy for a two-stage training framework that focuses on learning to decode document identifiers from queries and a strategy for mining hard negatives based on initial model's predictions. |
| Outcome: | The proposed model can generate ranked lists of potentially relevant document identifiers for a user query and then refine ranking through preference learning. |
Open Domain Question Answering over Tables via Dense Retrieval (2021.naacl-main)
Copied to clipboard
| 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. |