36 citations · 105 across the 10 of their papers we have counts for
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cs.CL2022★ 26 cited
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…
cs.CL2022★ 15 cited
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…
cs.CL2022★ 1 cited
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…