2 citations · 6 across the 10 of their papers we have counts for
5 papers · 1 filter
DAST: Context-Aware Compression in LLMs via Dynamic Allocation of Soft Tokens
Shaoshen Chen, Yangning Li, Zishan Xu +4
Large Language Models (LLMs) face computational inefficiencies and redundant processing when handling long context inputs, prompting a focus on compression techniques. While existi…
Loss-Aware Curriculum Learning for Chinese Grammatical Error Correction
Ding Zhang, Yangning Li, Lichen Bai +6
Chinese grammatical error correction (CGEC) aims to detect and correct errors in the input Chinese sentences. Recently, Pre-trained Language Models (PLMS) have been employed to imp…
Distill Visual Chart Reasoning Ability from LLMs to MLLMs
Wei He, Zhiheng Xi, Wanxu Zhao +6
Solving complex chart Q&A tasks requires advanced visual reasoning abilities in multimodal large language models (MLLMs), including recognizing key information from visual inputs a…
CLEME2.0: Towards Interpretable Evaluation by Disentangling Edits for Grammatical Error Correction
Jingheng Ye, Zishan Xu, Yinghui Li +9
The paper focuses on the interpretability of Grammatical Error Correction (GEC) evaluation metrics, which received little attention in previous studies. To bridge the gap, we intro…
Compression Represents Intelligence Linearly
Yuzhen Huang, Jinghan Zhang, Zifei Shan +1
There is a belief that learning to compress well will lead to intelligence. Recently, language modeling has been shown to be equivalent to compression, which offers a compelling ra…