Papers by Yushan Li

2 papers
PruMUX: Augmenting Data Multiplexing with Model Compression (2023.findings-acl)

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Challenge: Prior work has investigated methods like model pruning, knowledge distillation, and data multiplexing to increase model throughput without sacrificing accuracy.
Approach: They propose to combine structured pruning and data multiplexing methods to increase model throughput without sacrificing accuracy.
Outcome: The proposed method achieves 7.5-29.5X throughput improvement over a BERT-base model with accuracy threshold from 80% to 74%.
Can LLMs See Without Pixels? Benchmarking Spatial Intelligence from Textual Descriptions (2026.findings-acl)

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Challenge: Existing advances in Spatial Intelligence rely on vision-Language Models . however, a critical question remains: does spatial understanding originate from visual encoders?
Approach: They propose to evaluate the SI performance of Large Language Models without pixel-level input.
Outcome: The proposed benchmark challenges large language models to perform symbolic reasoning rather than visual pattern matching.

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