| Challenge: | Verbatim queries that do not adequately express the user's search intent are often lexical inadequacies. |
| Approach: | They propose a contrastive weighting model that learns to select the most useful expansion embeddings for semantic search. |
| Outcome: | The proposed model outperforms existing methods while maintaining its efficiency. |
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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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| Challenge: | Recent studies show query expansions generate hypothetical documents that answer queries as expansions. |
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| Challenge: | Existing methods for enhancing dense retrieval with query augmentation ignore the alignment between generation and ranking objectives. |
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Typo-Robust Representation Learning for Dense Retrieval (2023.acl-short)
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Panuthep Tasawong, Wuttikorn Ponwitayarat, Peerat Limkonchotiwat, Can Udomcharoenchaikit, Ekapol Chuangsuwanich, Sarana Nutanong
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| Challenge: | Existing methods for sparse and dense retrieval have limited success on popular datasets. |
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DVD: Dynamic Contrastive Decoding for Knowledge Amplification in Multi-Document Question Answering (2024.emnlp-main)
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| Challenge: | Large language models (LLMs) generate information with hallucinations due to uneven retrieval quality and irrelevant contents. |
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NPRF: A Neural Pseudo Relevance Feedback Framework for Ad-hoc Information Retrieval (D18-1)
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Enhancing Lexicon-Based Text Embeddings with Large Language Models (2025.acl-long)
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| Challenge: | Recent large language models (LLMs) have demonstrated exceptional performance on general-purpose text embedding tasks. |
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