Unsupervised Corpus Aware Language Model Pre-training for Dense Passage Retrieval (2022.acl-long)
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| 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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| Challenge: | Prior work fine-tunes deep LMs to encode text sequences into single dense vector representations, but dense encoders require a lot of data and sophisticated techniques to train and suffer in low data situations. |
| Approach: | They propose to pre-train Transformer language models (LMs) with a novel Transformer architecture, Condenser, where LM prediction CONditions on DENSE Representation. |
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| 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. |
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Liang Wang, Nan Yang, Xiaolong Huang, Binxing Jiao, Linjun Yang, Daxin Jiang, Rangan Majumder, Furu Wei
| Challenge: | SimLM uses a simple bottleneck architecture that learns to compress the passage information into a dense vector through self-supervised pre-training. |
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Shuqi Lu, Di He, Chenyan Xiong, Guolin Ke, Waleed Malik, Zhicheng Dou, Paul Bennett, Tie-Yan Liu, Arnold Overwijk
| Challenge: | Dense retrieval requires high-quality text sequence embeddings to support effective search in the representation space. |
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| Challenge: | Dense retrieval requires discriminative embeddings to represent the semantic relationship between query and document. |
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| Challenge: | Pre-trained language models have limited generalization capabilities and performance challenges. |
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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. |
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Boxin Wang, Wei Ping, Peng Xu, Lawrence McAfee, Zihan Liu, Mohammad Shoeybi, Yi Dong, Oleksii Kuchaiev, Bo Li, Chaowei Xiao, Anima Anandkumar, Bryan Catanzaro
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Dense Passage Retrieval: Is it Retrieving? (2024.findings-emnlp)
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| 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 . |
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ExpandR: Teaching Dense Retrievers Beyond Queries with LLM Guidance (2025.emnlp-main)
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| 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. |
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