1 citations · 1 across the 2 of their papers we have counts for
3 papers
cs.LG2026
Jump Start or False Start? A Theoretical and Empirical Evaluation of LLM-initialized Bandits
Adam Bayley, Xiaodan Zhu, Raquel Aoki +2
The recent advancement of Large Language Models (LLMs) offers new opportunities to generate user preference data to warm-start bandits. Recent studies on contextual bandits with LL…
cs.CL2024★ 1 cited
Mitigating Social Biases in Language Models through Unlearning
Omkar Dige, Diljot Singh, Tsz Fung Yau +4
Mitigating bias in language models (LMs) has become a critical problem due to the widespread deployment of LMs. Numerous approaches revolve around data pre-processing and fine-tuni…
cs.CL2024
Red-Teaming for Inducing Societal Bias in Large Language Models
Chu Fei Luo, Ahmad Ghawanmeh, Bharat Bhimshetty +4
Ensuring the safe deployment of AI systems is critical in industry settings where biased outputs can lead to significant operational, reputational, and regulatory risks. Thorough e…