Recent works have demonstrated the effectiveness of self-alignment in which a
large language model is, by itself, aligned to follow general instructions
through the automatic generation of instructional data using a handful of
human-written seeds. Instead of general alignment, in this work, we focus on
self-alignment for expert domain specialization (e.g., biomedicine),
discovering it to be very effective for improving zero-shot and few-shot
performance in target domains of interest. As a preliminary, we first present
the benchmark results of existing aligned models within a specialized domain,
which reveals the marginal effect that "generic" instruction-following training
has on downstream expert domains' performance. To remedy this, we explore
self-specialization that leverages domain-specific unlabelled data and a few
labeled seeds for the self-alignment process. When augmented with retrieval to
reduce hallucination and enhance concurrency of the alignment,
self-specialization offers an effective (and efficient) way of "carving out" an
expert model out of a "generalist", pre-trained LLM where different domains of
expertise are originally combined in a form of "superposition". Our
experimental results on a biomedical domain show that our self-specialized
model (30B) outperforms its base model, MPT-30B by a large margin and even
surpasses larger popular models based on LLaMA-65B, highlighting its potential
and practicality for specialization, especially considering its efficiency in
terms of data and parameters.

通过自我对齐可以在专业领域中提高零样本和小样本性能，利用特定领域的无标签数据和一些标记的种子进行自我特化，有效地从预训练大型语言模型中提取出专家模型。