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.CL2025
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…
cs.CL2024
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…