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Vision-Language Models Enhance Document Retrieval by Judging Negative Examples

By PulseAugur Editorial · [1 sources] ·

Researchers have explored how to improve visual document retrieval systems by leveraging vision-language models (VLMs). Instead of solely using VLMs to enrich positive examples, the study found that using VLMs to identify and judge hard negative examples significantly boosted retrieval performance. This approach, which involves distilling the VLM's judgments on irrelevant pages, improved the nDCG@5 score from 55.2 to 62.6. The findings suggest that VLM supervision is more effective when applied to the negative examples, which are largely unaddressed by current labeling methods. AI

IMPACT Improves the effectiveness of visual document retrieval systems by leveraging VLMs for negative example identification.

RANK_REASON The item is an academic paper detailing a new method for improving visual document retrieval systems. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.IR (Information Retrieval) →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Vision-Language Models Enhance Document Retrieval by Judging Negative Examples

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The item is an academic paper detailing a new method for improving visual document retrieval systems. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.IR (Information Retrieval) TIER_1 English(EN) · Han Xiao ·

    What Transfers from a VLM Teacher? Comparing Supervision Signals for Visual Document Retrieval

    Visual document retrievers are trained contrastively: each query is matched to one page labelled relevant - the positive - and pushed away from negatives, pages presumed irrelevant. Recent methods distil a vision-language model (VLM) teacher into the retriever by enriching that p…

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