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New dataset ReGround tackles multimodal evidence grounding for reviewer comments

By PulseAugur Editorial · [1 sources] ·

Researchers have introduced ReGround, a new dataset designed to address the challenge of linking reviewer comments to specific evidence within long, multimodal academic papers. The dataset comprises over 10,000 reviewer comments and more than 16,000 corresponding evidence snippets from thousands of papers. Initial experiments indicate that simple retrieval methods struggle with this task, highlighting the difficulty of inferring evidence types and the importance of multimodal information for accurate grounding. AI

IMPACT This dataset could improve AI's ability to understand and process complex scientific documents, aiding researchers and reviewers.

RANK_REASON The item describes a new dataset and methodology for a specific research problem in scientific document understanding. [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 →

New dataset ReGround tackles multimodal evidence grounding for reviewer comments

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The item describes a new dataset and methodology for a specific research problem in scientific document understanding. [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) · Iryna Gurevych ·

    ReGround: Grounding Reviewer Comments in Multimodal Evidence

    Reviewer comments naturally relate to specific parts of the reviewed paper, yet grounding these comments to the underlying evidence is difficult due to long multimodal documents. Existing benchmarks do not capture this setting and largely focus on explicit, information-seeking qu…

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