  In this paper, we explore a new way for user targeting, where non-expert
marketers could select their target users solely given demands in natural
language form. The key to this issue is how to transform natural languages into
practical structured logical languages, i.e., the structured understanding of
marketer demands. In practical scenarios, the demands of non-expert marketers
are often abstract and diverse. Considering the impressive natural language
processing ability of large language models (LLMs), we try to leverage LLMs to
solve this issue. To stimulate the LLMs' reasoning ability, the
chain-of-thought (CoT) prompting method is widely used, but existing methods
still have some limitations in our scenario: (1) Previous methods either use
simple "Let's think step by step" spells or provide fixed examples in
demonstrations without considering compatibility between prompts and concrete
questions, making LLMs ineffective when the marketers' demands are abstract and
diverse. (2) Previous methods are often implemented in closed-source models or
excessively large models, which is not suitable in industrial practical
scenarios. Based on these, we propose ARALLM (i.e., Analogical Reasoning
Augmented Large Language Models) consisting of two modules: Analogical
Reasoning based Prompting and Reasoning-Augmented Multi-Task Model
Distillation. Part of our data and code can be found at
https://github.com/alipay/Analogic-Reasoning-Augmented-Large-Language-Model.
