Self-Specialization: Uncovering Latent Expertise within Large Language
  Models
Junmo Kang Hongyin Luo Yada Zhu James Glass David Cox Alan Ritter Rogerio Feris Leonid Karlinsky
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.