Existing dialogue models may encounter scenarios which are not
well-represented in the training data, and as a result generate responses that
are unnatural, inappropriate, or unhelpful. We propose the "Ask an Expert"
framework in which the model is trained with access to an "expert" which it can
consult at each turn. Advice is solicited via a structured dialogue with the
expert, and the model is optimized to selectively utilize (or ignore) it given
the context and dialogue history. In this work the expert takes the form of an
LLM. We evaluate this framework in a mental health support domain, where the
structure of the expert conversation is outlined by pre-specified prompts which
reflect a reasoning strategy taught to practitioners in the field. Blenderbot
models utilizing "Ask an Expert" show quality improvements across all expert
sizes, including those with fewer parameters than the dialogue model itself.
Our best model provides a $\sim 10\%$ improvement over baselines, approaching
human-level scores on "engingingness" and "helpfulness" metrics.

本论文提出 “请教专家” 框架，通过对话模型与专家的结合，对话模型可以通过结构化对话咨询专家并优化模型的效果， 通过在心理健康支持领域的应用表明，使用此框架可以提高对话模型的质量，并取得了人类水平的得分。