| Challenge: | Existing studies focus on adapting either the retriever or the reader, but this approach is more focused on adaptation of the query itself. |
| Approach: | They propose a new framework for retrieval-augmented Large Language Models . they propose rewrite-retrieve-read instead of retrieve-then-read . |
| Outcome: | The proposed framework improves performance on downstream tasks, open-domain QA and multiple-choice QA. |
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| Challenge: | Existing approaches to rewrite context-dependent queries lack sufficient information for optimal retrieval performance. |
| Approach: | They propose to use large language models (LLMs) as query rewriters to generate informative queries through well-designed instructions. |
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Reimagining Retrieval Augmented Language Models for Answering Queries (2023.findings-acl)
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Wang-Chiew Tan, Yuliang Li, Pedro Rodriguez, Richard James, Xi Victoria Lin, Alon Halevy, Wen-tau Yih
| Challenge: | Large language models (LLMs) are expensive to train, deploy, and maintain, both financially and in terms of environmental impact. |
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Retrieval-Augmented Retrieval: Large Language Models are Strong Zero-Shot Retriever (2024.findings-acl)
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Tao Shen, Guodong Long, Xiubo Geng, Chongyang Tao, Yibin Lei, Tianyi Zhou, Michael Blumenstein, Daxin Jiang
| Challenge: | Large-scale retrieval is indispensable in information-seeking tasks such as open-domain question answering and knowledgegrounded dialogue. |
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| Challenge: | Large Language Models (LLMs) are too large to be fine-tuned with budget constraints and some are only accessible via APIs. |
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Embedding-Informed Adaptive Retrieval-Augmented Generation of Large Language Models (2025.coling-main)
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| Challenge: | Retrieval-augmented large language models excel in various NLP tasks but are not always helpful when the knowledge required is absent in the model. |
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MiniELM: A Lightweight and Adaptive Query Rewriting Framework for E-Commerce Search Optimization (2025.findings-acl)
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| Challenge: | Existing methods for rewriting query terms struggle with natural language understanding . generative methods face high inference latency and cost in offline settings . |
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| Challenge: | Recent Large Language Models (LLMs) generate factually incorrect answers based on their parametric memory. |
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Query Optimization for Parametric Knowledge Refinement in Retrieval-Augmented Large Language Models (2025.findings-emnlp)
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| Challenge: | Extract-Refine-Retrieve-Read is a query optimization framework for large language models . it is designed to bridge the pre-retrieval information gap in Retriev-Augmented Generation systems . |
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| Challenge: | Existing methods for retrieval of information excel at textual and semantic matching but struggle in reasoning-intensive retrieval tasks. |
| Approach: | They propose a family of small-scale language models for query reasoning and rewriting in reasoning-intensive retrieval. |
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Query2doc: Query Expansion with Large Language Models (2023.emnlp-main)
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| Challenge: | Existing methods for sparse and dense retrieval have limited success on popular datasets. |
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