Papers by Fengnan Li
Automatic and Reliable Evaluation for Academic Caption-to-Figure Generation with LMMs (2026.acl-long)
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| Challenge: | Existing datasets for evaluating text-to-image generation focus mostly on real-life images, which poses challenges for assessing academic figure generation given real scientific captions. |
| Approach: | They propose a dataset that first provides a Holistic Evaluation for Academic caption-to-Figure Generation (HE4AFG) they collect real figure captions from 8 scientific domains and generate 3,900 evaluation samples . |
| Outcome: | The proposed model provides high-quality human ratings in terms of three aspects—scientific aesthetic (SA), topic relevance (TR), and attribute correctness (AC). |
IRIS: Interpretable Retrieval-Augmented Classification for Long Interspersed Document Sequences (2025.acl-long)
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| Challenge: | Existing models for document classification struggle with long-text processing due to quadratic computational complexity in the self-attention module. |
| Approach: | They propose a framework that utilizes retrieval to efficiently classify long documents . they use a quadratic attention matrix to capture dependencies between tokens in an input sequence . |
| Outcome: | The proposed framework excels in clinical note disease risk prediction tasks . it can process arbitrarily long documents without increasing computational cost and trainable on a single GPU. |