5 citations · 5 across the 3 of their papers we have counts for
3 papers
cs.LG2025
BPL: Bias-adaptive Preference Distillation Learning for Recommender System
SeongKu Kang, Jianxun Lian, Dongha Lee +6
Recommender systems suffer from biases that cause the collected feedback to incompletely reveal user preference. While debiasing learning has been extensively studied, they mostly…
cs.CL2025
STEPER: Step-wise Knowledge Distillation for Enhancing Reasoning Ability in Multi-Step Retrieval-Augmented Language Models
Kyumin Lee, Minjin Jeon, Sanghwan Jang +1
Answering complex real-world questions requires step-by-step retrieval and integration of relevant information to generate well-grounded responses. However, existing knowledge dist…
cs.IR2025★ 5 cited
Uncertainty Quantification and Decomposition for LLM-based Recommendation
Wonbin Kweon, Sanghwan Jang, SeongKu Kang +1
Despite the widespread adoption of large language models (LLMs) for recommendation, we demonstrate that LLMs often exhibit uncertainty in their recommendations. To ensure the trust…