| 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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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. |
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On Synthetic Data Strategies for Domain-Specific Generative Retrieval (2025.acl-long)
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| 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. |
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Sampling Matters! An Empirical Study of Negative Sampling Strategies for Learning of Matching Models in Retrieval-based Dialogue Systems (D19-1)
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| Challenge: | Existing studies focus on constructing a matching model with sophisticated neural architectures, but do little to how to effectively learn such architectures from data. |
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Boosting Data Utilization for Multilingual Dense Retrieval (2025.emnlp-main)
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| 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. |
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Sentence-aware Contrastive Learning for Open-Domain Passage Retrieval (2022.acl-long)
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| Challenge: | Existing studies focus on improving negative sampling strategy or extra pretraining for dense passage representations, but these studies are not capturing passage with internal representation conflicts. |
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Unsupervised Dense Retrieval with Relevance-Aware Contrastive Pre-Training (2023.findings-acl)
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| 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. |
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A Comprehensive Taxonomy of Negation for NLP and Neural Retrievers (2025.findings-emnlp)
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| Challenge: | a new taxonomy of negation is proposed to improve neural information retrieval models . negation types are covered in existing datasets, allowing for faster convergence . |
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Retrieval-based Language Models and Applications (2023.acl-tutorials)
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| Challenge: | In this tutorial, we will provide a comprehensive overview of retrieval-based language models. |
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| Outcome: | This tutorial will provide a comprehensive overview of recent advances in retrieval-based language models. |
Leveraging LLMs for Synthesizing Training Data Across Many Languages in Multilingual Dense Retrieval (2024.naacl-long)
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| Challenge: | et al., 2020: performance of dense retrieval models in multilingual retrieval is limited due to uneven and scarce training data available across multiple languages. |
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Scalable Evaluation and Improvement of Document Set Expansion via Neural Positive-Unlabeled Learning (2021.eacl-main)
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| Challenge: | Current methods for document set expansion for large collections are based on word-frequency or bag-of-words document similarity metrics. |
| Approach: | They propose to extend the IR approach by treating the problem as an instance of positive-unlabeled (PU) learning . they propose solutions for each challenge and empirically validate them with ablation tests . |
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