Papers by Xueyan Wang
HowToNarrate: A General-Domain Benchmark for Synchronized Video Narration with External Knowledge (2026.acl-long)
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| Challenge: | Existing MLLMs overemphasize knowledge retrieval while neglecting prior context, causing redundancy and incoherence. |
| Approach: | They propose a framework that combines context compression, knowledge retrieval, and narration generation to improve models' performance. |
| Outcome: | The proposed method significantly improves MLLM performance over existing models. |
UORA: Uniform Orthogonal Reinitialization Adaptation in Parameter Efficient Fine-Tuning of Large Models (2025.acl-long)
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| Challenge: | Existing methods such as LoRA and VeRA use a low-rank approximation method that reduces the number of trainable parameters without compromising performance. |
| Approach: | They propose a parameter-efficient fine-tuning approach that leverages a low-rank approximation method that reduces the number of trainable parameters without compromising performance. |
| Outcome: | The proposed approach outperforms existing methods on GLUE and E2E benchmarks and is effective in instruction-tuning large language models and image classification models. |