1 citations · 1 across the 3 of their papers we have counts for
3 papers
cs.CL2024
Bias and Volatility: A Statistical Framework for Evaluating Large Language Model's Stereotypes and the Associated Generation Inconsistency
Yiran Liu, Ke Yang, Zehan Qi +3
We present a novel statistical framework for analyzing stereotypes in large language models (LLMs) by systematically estimating the bias and variation in their generation. Current…
cs.CL2023★ 1 cited
Tuna: Instruction Tuning using Feedback from Large Language Models
Haoran Li, Yiran Liu, Xingxing Zhang +2
Instruction tuning of open-source large language models (LLMs) like LLaMA, using direct outputs from more powerful LLMs such as Instruct-GPT and GPT-4, has proven to be a cost-effe…
cs.CL2022
Does Debiasing Inevitably Degrade the Model Performance
Yiran Liu, Xiao Liu, Haotian Chen +1
Gender bias in language models has attracted sufficient attention because it threatens social justice. However, most of the current debiasing methods degraded the model's performan…