1 citations · 1 across the 17 of their papers we have counts for
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Turning Trash into Treasure: Accelerating Inference of Large Language Models with Token Recycling
Xianzhen Luo, Yixuan Wang, Qingfu Zhu +4
Massive parameters of LLMs have made inference latency a fundamental bottleneck. Speculative decoding represents a lossless approach to accelerate inference through a guess-and-ver…
Improving Grammatical Error Correction via Contextual Data Augmentation
Yixuan Wang, Baoxin Wang, Yijun Liu +3
Nowadays, data augmentation through synthetic data has been widely used in the field of Grammatical Error Correction (GEC) to alleviate the problem of data scarcity. However, these…
Make Some Noise: Unlocking Language Model Parallel Inference Capability through Noisy Training
Yixuan Wang, Xianzhen Luo, Fuxuan Wei +6
Existing speculative decoding methods typically require additional model structure and training processes to assist the model for draft token generation. This makes the migration o…
LM-Combiner: A Contextual Rewriting Model for Chinese Grammatical Error Correction
Yixuan Wang, Baoxin Wang, Yijun Liu +2
Over-correction is a critical problem in Chinese grammatical error correction (CGEC) task. Recent work using model ensemble methods based on voting can effectively mitigate over-co…