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cs.CL2024
Enhance Robustness of Language Models Against Variation Attack through Graph Integration
Zi Xiong, Lizhi Qing, Yangyang Kang +5
The widespread use of pre-trained language models (PLMs) in natural language processing (NLP) has greatly improved performance outcomes. However, these models' vulnerability to adv…
cs.CL2024
From Model-centered to Human-Centered: Revision Distance as a Metric for Text Evaluation in LLMs-based Applications
Yongqiang Ma, Lizhi Qing, Jiawei Liu +5
Evaluating large language models (LLMs) is fundamental, particularly in the context of practical applications. Conventional evaluation methods, typically designed primarily for LLM…
cs.CL2024★ 2 cited
Evolving Knowledge Distillation with Large Language Models and Active Learning
Chengyuan Liu, Yangyang Kang, Fubang Zhao +4
Large language models (LLMs) have demonstrated remarkable capabilities across various NLP tasks. However, their computational costs are prohibitively high. To address this issue, p…