  Large Language Models (LLMs) have exhibited impressive capabilities in
various tasks, yet their vast parameter sizes restrict their applicability in
resource-constrained settings. Knowledge distillation (KD) offers a viable
solution by transferring expertise from large teacher models to compact student
models. However, traditional KD techniques face specific challenges when
applied to LLMs, including restricted access to LLM outputs, significant
teacher-student capacity gaps, and the inherited mis-calibration issue. In this
work, we present PLaD, a novel preference-based LLM distillation framework.
PLaD exploits the teacher-student capacity discrepancy to generate
pseudo-preference pairs where teacher outputs are preferred over student
outputs. Then, PLaD leverages a ranking loss to re-calibrate student's
estimation of sequence likelihood, which steers the student's focus towards
understanding the relative quality of outputs instead of simply imitating the
teacher. PLaD bypasses the need for access to teacher LLM's internal states,
tackles the student's expressivity limitations, and mitigates the student
mis-calibration issue. Through extensive experiments on two sequence generation
tasks and with various LLMs, we demonstrate the effectiveness of our proposed
PLaD framework.
