Papers by Yongyi He
Granular Entity Mapper: Advancing Fine-grained Multimodal Named Entity Recognition and Grounding (2024.findings-emnlp)
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| Challenge: | Existing methods for fine-grained content extraction are limited by long-tailed distribution of textual entity categories and performance of object detectors. |
| Approach: | They propose a multi-granularity entity recognition module and a reranking module to integrate hierarchical information of entity categories, visual cues, and external textual resources collectively. |
| Outcome: | The proposed framework achieves state-of-the-art on the fine-grained content extraction task. |
In-Context Former: Lightning-fast Compressing Context for Large Language Model (2024.findings-emnlp)
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| Challenge: | Existing methods to reduce inference costs of transformer-based large language models entail quadratic complexity . et al., 2017): transformer-derived large language model performance is a major challenge. |
| Approach: | They propose a method that compresses long contexts into short soft prompts . they use the self-attention mechanism of the large model to extract and condense information . |
| Outcome: | The proposed method reduces compression costs by 68 to 112 times while achieving 90% of baseline performance. |