BLADE: Enhancing Black-box Large Language Models with Small
  Domain-Specific Models
Haitao Li Qingyao Ai Jia Chen Qian Dong Zhijing Wu Yiqun Liu Chong Chen Qi Tian
Large Language Models (LLMs) like ChatGPT and GPT-4 are versatile and capable
of addressing a diverse range of tasks. However, general LLMs, which are
developed on open-domain data, may lack the domain-specific knowledge essential
for tasks in vertical domains, such as legal, medical, etc. To address this
issue, previous approaches either conduct continuous pre-training with
domain-specific data or employ retrieval augmentation to support general LLMs.
Unfortunately, these strategies are either cost-intensive or unreliable in
practical applications. To this end, we present a novel framework named BLADE,
which enhances Black-box LArge language models with small Domain-spEcific
models. BLADE consists of a black-box LLM and a small domain-specific LM. The
small LM preserves domain-specific knowledge and offers specialized insights,
while the general LLM contributes robust language comprehension and reasoning
capabilities. Specifically, our method involves three steps: 1) pre-training
the small LM with domain-specific data, 2) fine-tuning this model using
knowledge instruction data, and 3) joint Bayesian optimization of the general
LLM and the small LM. Extensive experiments conducted on public legal and
medical benchmarks reveal that BLADE significantly outperforms existing
approaches. This shows the potential of BLADE as an effective and
cost-efficient solution in adapting general LLMs for vertical domains.