2 citations · 2 across the 5 of their papers we have counts for
5 papers · 1 filter
Instruction Quality Matters: Refining Instructions for Effective Preference Learning
Seohyeong Lee, Hwaran Lee, Buru Chang
Preference learning optimizes models using response pairs, yet the informativeness of these pairs is fundamentally shaped by the instructions from which they are generated. We iden…
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…
Guaranteed Generation from Large Language Models
Minbeom Kim, Thibaut Thonet, Jos Rozen +3
As large language models (LLMs) are increasingly used across various applications, there is a growing need to control text generation to satisfy specific constraints or requirement…
KorNAT: LLM Alignment Benchmark for Korean Social Values and Common Knowledge
Jiyoung Lee, Minwoo Kim, Seungho Kim +4
For Large Language Models (LLMs) to be effectively deployed in a specific country, they must possess an understanding of the nation's culture and basic knowledge. To this end, we i…