2 citations · 2 across the 5 of their papers we have counts for
3 papers · 1 filter
BLEnD-Vis: Benchmarking Multimodal Cultural Understanding in Vision Language Models
Bryan Chen Zhengyu Tan, Zheng Weihua, Zhengyuan Liu +4
As vision-language models (VLMs) are deployed globally, their ability to understand culturally situated knowledge becomes essential. Yet, existing evaluations largely assess static…
Alignment Data Map for Efficient Preference Data Selection and Diagnosis
Seohyeong Lee, Eunwon Kim, Hwaran Lee +1
Human preference data is essential for aligning large language models (LLMs) with human values, but collecting such data is often costly and inefficient-motivating the need for eff…
Drift: Decoding-time Personalized Alignments with Implicit User Preferences
Minbeom Kim, Kang-il Lee, Seongho Joo +3
Personalized alignments for individual users have been a long-standing goal in large language models (LLMs). We introduce Drift, a novel framework that personalizes LLMs at decodin…