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cs.CL2025
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…
cs.CL2025
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…