Papers with document identifiers
Large Language Models are Built-in Autoregressive Search Engines (2023.findings-acl)
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| Challenge: | Existing dual-encoder dense retrievers obtain representations for questions and documents independently, allowing only shallow interactions between them. |
| Approach: | They propose to use large language models to generate URLs for document retrieval by following human instructions. |
| Outcome: | The proposed method achieves better retrieval performance than existing retrieval approaches on open-domain question answering benchmarks. |
Resource Interoperability for Sustainable Benchmarking: The Case of Events (L18-1)
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| Challenge: | Despite efforts to improve interoperability, there are still problems with benchmark corpora that are hampered by too laborious conversion steps. |
| Approach: | They assess aspects of interoperability at the document-level across 20 annotated corpora and compare their compatibility and consistency across the corpors. |
| Outcome: | The proposed framework enables the analysis of document intersections between the corpora and shows their compatibility and consistency across the corpus. |
TOME: A Two-stage Approach for Model-based Retrieval (2023.acl-long)
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| 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. |
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. |
| Outcome: | The proposed model can generate ranked lists of potentially relevant document identifiers for a user query and then refine ranking through preference learning. |