Papers with text retrieval methods
ELIOT: Zero-Shot Video-Text Retrieval through Relevance-Boosted Captioning and Structural Information Extraction (2025.naacl-srw)
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| Challenge: | Recent advances in video-text retrieval (VTR) have relied on supervised learning and fine-tuning. |
| Approach: | They propose a zero-shot video-text retrieval framework that leverages off-the-shelf captioners, large language models, and text retrieval methods without additional training or annotated data. |
| Outcome: | The proposed framework outperforms existing methods on video-text retrieval benchmarks without data. |
Neurocache: Efficient Vector Retrieval for Long-range Language Modeling (2024.naacl-long)
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| Challenge: | Recent research shows that retrieval-augmented models with shorter contexts (4K tokens) can match the performance of models with longer contexts (16K/32K token) |
| Approach: | They introduce an approach to extend the effective context size of large language models by using an external vector cache to store past states. |
| Outcome: | The proposed method improves on models trained from scratch and pre-trained models. |
Unifying Multimodal Retrieval via Document Screenshot Embedding (2024.emnlp-main)
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| Challenge: | Existing document retrieval pipelines require document parsing and content extraction to prepare input for indexing. |
| Approach: | They propose a retrieval paradigm that regards document screenshots as a unified input format . they leverage a large vision-language model to directly encode document screenshot into dense representations . |
| Outcome: | The proposed method outperforms existing retrieval pipelines in a text-intensive context. |