| Challenge: | Recent research has focused on model-based retrieval, which discards the index in the traditional retrieval model and memorizes the candidate corpora using model parameters. |
| Approach: | They propose a model-based retrieval approach that discards the index in the traditional retrieval model and memorizes the candidate corpora using model parameters. |
| Outcome: | The proposed approach eliminates the index in the traditional retrieval model and memorizes the candidate corpora using model parameters. |
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| Challenge: | Existing text-image approaches use pre-trained vision-language representations for text retrieval . however, these models pose non-trivial memory requirements and substantial indexing time . |
| Approach: | They propose a framework to compress large pre-trained dual-encoders for lightweight text-image retrieval. |
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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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DiffusionRet: Diffusion-Enhanced Generative Retriever using Constrained Decoding (2023.findings-emnlp)
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| Challenge: | Generative retrieval methods have suffered from the lack of the intermediate reasoning step . generative retrieval uses sequence-to-sequence diffusion models to map a query to relevant docids . |
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How Does Generative Retrieval Scale to Millions of Passages? (2023.emnlp-main)
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Ronak Pradeep, Kai Hui, Jai Gupta, Adam Lelkes, Honglei Zhuang, Jimmy Lin, Donald Metzler, Vinh Tran
| Challenge: | generative retrieval is a new paradigm for information retrieval, enabling a sequence-to-sequence model with a single Transformer . generative encoders have been used on small corpora, but only on large ones . |
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Auto Search Indexer for End-to-End Document Retrieval (2023.findings-emnlp)
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| Challenge: | Generative retrieval heavily relies on the “preprocessed” document identifiers, thus limiting its retrieval performance and ability to retrieve new documents. |
| Approach: | They propose a fully end-to-end retrieval paradigm that can learn the best docids for existing and new documents automatically via a semantic indexing module. |
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GLEN: Generative Retrieval via Lexical Index Learning (2023.emnlp-main)
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| Challenge: | Existing methods for document retrieval bypass auxiliary index structures and can be optimized through end-to-end learning. |
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Enhancing Generative Retrieval with Reinforcement Learning from Relevance Feedback (2023.emnlp-main)
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| Challenge: | End-to-end generative retrieval models produce document identifiers in response to a query . however, this approach has two challenges: an overemphasis on top-1 results at the expense of overall ranking quality. |
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Enhancing Retrieval-Augmented Large Language Models with Iterative Retrieval-Generation Synergy (2023.findings-emnlp)
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| Challenge: | Recent work has proposed to improve relevance modeling by having large language models actively involved in retrieval, i.e., to guide retrieval with generation. |
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Keyphrase Generation for Scientific Document Retrieval (2020.acl-main)
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| Challenge: | Sequence-to-sequence models have been used to generate keyphrases, but it is unclear whether they are reliable enough for document retrieval. |
| Approach: | They propose a framework for extrinsic evaluation that allows for a better understanding of the limitations of keyphrase generation models. |
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HYRR: Hybrid Infused Reranking for Passage Retrieval (2024.lrec-main)
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| Challenge: | Existing passage retrieval systems typically adopt a two-stage retrieve-then-rerank pipeline. |
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