Papers by Yaowei Li
HiPrune: Hierarchical Attention for Efficient Token Pruning in Vision-Language Models (2026.findings-acl)
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| Challenge: | Existing methods for visual token pruning lack insight into the intrinsic property of the vision encoder . et al., 2017: 99.3% of task accuracy with only 1/3 of the tokens. |
| Approach: | They propose a model-agnostic token pruning method that trains without training . they propose 'HiPrune' method which prunes visual tokens according to their attention . |
| Outcome: | The proposed method achieves 99.3% of task accuracy with only 1/3 of the tokens . it reduces inference FLOPs by 58.7% and maintains 99.99% accuracy with 2/9 tokens. |
Accelerating Multiple Intent Detection and Slot Filling via Targeted Knowledge Distillation (2023.findings-emnlp)
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| Challenge: | Existing non-autoregressive Spoken Language Understanding models suffer from multi-modality problem . current methods have little prior knowledge about the reference during inference . |
| Approach: | They propose a Targeted Knowledge Distillation Framework (TKDF) for multi-intent SLU that utilizes the knowledge distillation method to improve the performance. |
| Outcome: | The proposed model outperforms existing models on two public multi-intent datasets while speeding up by over 4.5 times. |
Towards Multi-modal Sarcasm Detection via Disentangled Multi-grained Multi-modal Distilling (2024.lrec-main)
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| Challenge: | Existing approaches to sarcasm detection focus on textual and intra-modal incongruity . mainstream approaches process input of each modality in a holistic manner, resulting in redundant and unrefined information. |
| Approach: | They propose a framework for multi-modal sarcasm detection that disentangles modality representations into latent spaces and conducts multi-grained knowledge distilling. |
| Outcome: | The proposed framework overpowers existing methods on a common benchmark. |
Easy Dataset: A Unified and Extensible Framework for Synthesizing LLM Fine-Tuning Data from Unstructured Documents (2025.emnlp-demos)
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| Challenge: | Existing data synthesis tools struggle to extract reliable fine-tuning data from heterogeneous documents. |
| Approach: | They propose a framework for synthesizing fine-tuning data from unstructured documents via an intuitive graphical user interface. |
| Outcome: | The proposed framework can extract reliable data from unstructured documents via an intuitive graphical user interface (GUI) it leverages persona-driven prompting approach to generate diverse question-answer pairs using public-available LLMs. |
Soul-Mix: Enhancing Multimodal Machine Translation with Manifold Mixup (2024.acl-long)
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| Challenge: | Multimodal machine translation (MMT) aims to improve the performance of machine translation with the help of visual information. |
| Approach: | They propose a multimodal machine translation mixup method that integrates visual information into conventional text-only neural machine translation systems. |
| Outcome: | The proposed method outperforms existing models on a multi-directional dataset with fewer parameters and achieves new state-of-the-art performance. |
VideoVista-CulturalLingo: 360° Horizons-Bridging Cultures, Languages, and Domains in Video Comprehension (2025.acl-long)
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| Challenge: | Existing video evaluation benchmarks focus on a single language, typically English, and feature videos rooted in Western cultural contexts. |
| Approach: | They propose a video evaluation benchmark designed to bridge cultural, linguistic, and domain divide in video comprehension. |
| Outcome: | The proposed video evaluation benchmark bridges cultural, linguistic, and domain divides . existing benchmarks only feature videos from YouTube, Shutterstock, or established video datasets based on cultural diversity . |
Latent-Condensed Transformer for Efficient Long Context Modeling (2026.acl-long)
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| Challenge: | Existing approaches address these bottlenecks separately: Multi-head Latent Attention (MLA) reduces the KV cache by projecting tokens into a low-dimensional latent space, while sparse attention reduces computation. |
| Approach: | They propose a Latent-Condensed Attention mechanism that performs structured context condensation directly within MLA's latent space. |
| Outcome: | The proposed approach reduces KV cache size and attention cost without adding parameters. |