Papers by Suqing Wang
From Parameters to Performance: A Data-Driven Study on LLM Structure and Development (2025.emnlp-main)
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| Challenge: | Large language models have revolutionized a wide range of domains, driving significant advancements in both technology and real-world applications. |
| Approach: | They present a large-scale dataset encompassing diverse open-source LLM structures and their performance across multiple benchmarks. |
| Outcome: | The proposed model validates the relationship between structural configurations and performance across multiple benchmarks and further corroborates the findings using mechanistic interpretability techniques. |
CoViPAL: Layer-wise Contextualized Visual Token Pruning for Large Vision-Language Models (2025.findings-emnlp)
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| Challenge: | Existing methods to prune redundant vision tokens struggle in shallow layers due to the lack of contextual information. |
| Approach: | They propose a layer-wise contextualized visual token pruning method that uses a plug-and-play Pruning Module to prune redundant vision tokens. |
| Outcome: | The proposed method outperforms training-free pruning methods under equal token budgets and surpasses training based methods with comparable supervision. |