36 citations · 105 across the 10 of their papers we have counts for
10 papers
Evaluating Psychological Safety of Large Language Models
Xingxuan Li, Yutong Li, Lin Qiu +2
In this work, we designed unbiased prompts to systematically evaluate the psychological safety of large language models (LLMs). First, we tested five different LLMs by using two pe…
Is GPT-3 a Good Data Annotator?
Bosheng Ding, Chengwei Qin, Linlin Liu +4
Data annotation is the process of labeling data that could be used to train machine learning models. Having high-quality annotation is crucial, as it allows the model to learn the…
Towards Robust Low-Resource Fine-Tuning with Multi-View Compressed Representations
Linlin Liu, Xingxuan Li, Megh Thakkar +4
Due to the huge amount of parameters, fine-tuning of pretrained language models (PLMs) is prone to overfitting in the low resource scenarios. In this work, we present a novel metho…
Enhancing Multilingual Language Model with Massive Multilingual Knowledge Triples
Linlin Liu, Xin Li, Ruidan He +3
Knowledge-enhanced language representation learning has shown promising results across various knowledge-intensive NLP tasks. However, prior methods are limited in efficient utiliz…
On the Effectiveness of Adapter-based Tuning for Pretrained Language Model Adaptation
Ruidan He, Linlin Liu, Hai Ye +6
Adapter-based tuning has recently arisen as an alternative to fine-tuning. It works by adding light-weight adapter modules to a pretrained language model (PrLM) and only updating t…
Towards Multi-Sense Cross-Lingual Alignment of Contextual Embeddings
Linlin Liu, Thien Hai Nguyen, Shafiq Joty +2
Cross-lingual word embeddings (CLWE) have been proven useful in many cross-lingual tasks. However, most existing approaches to learn CLWE including the ones with contextual embeddi…
A paper's corresponding affiliation and first affiliation are consistent at the country level in Web of Science
Jianfei Yu, Chunxiao Yin, Linlin Liu +1
The purpose of this study is to explore the relationship between the first affiliation and the corresponding affiliation at the different levels via the scientometric analysis We s…
DAGA: Data Augmentation with a Generation Approach for Low-resource Tagging Tasks
Bosheng Ding, Linlin Liu, Lidong Bing +5
Data augmentation techniques have been widely used to improve machine learning performance as they enhance the generalization capability of models. In this work, to generate high q…
The dominance of big teams in China's scientific output
Linlin Liu, Jianfei Yu, Junming Huang +2
Modern science is dominated by scientific productions from teams. A recent finding shows that teams with both large and small sizes are essential in research, prompting us to analy…
Hierarchical Pointer Net Parsing
Linlin Liu, Xiang Lin, Shafiq Joty +2
Transition-based top-down parsing with pointer networks has achieved state-of-the-art results in multiple parsing tasks, while having a linear time complexity. However, the decoder…