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This work studies the ability of LLMs to generate edit-inducing questions whose answer will improve a paper draft and compares the helpfulness of questions from GPT models with or without full paper context to that of human reviewers.
Scientific figures are designed to communicate information visually, yet MLLMs typically explain them by translating their visual content back into text. This requires readers to manually map the resulting explanations back to the figure. Inspired by how people present visual information, we introduce FigAct, a framewo...
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