Summary of Know Your Needs Better: Towards Structured Understanding Of Marketer Demands with Analogical Reasoning Augmented Llms, by Junjie Wang et al.
Know Your Needs Better: Towards Structured Understanding of Marketer Demands with Analogical Reasoning Augmented LLMs
by Junjie Wang, Dan Yang, Binbin Hu, Yue Shen, Wen Zhang, Jinjie Gu
First submitted to arxiv on: 9 Jan 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper presents a novel approach to user targeting, enabling non-expert marketers to select target users solely based on natural language demands. The key challenge is transforming these demands into structured logical languages, which requires understanding the marketer’s intentions. To address this, the authors leverage large language models (LLMs) and propose ARALLM, a two-module framework consisting of analogical reasoning-based prompting and reasoning-augmented multi-task model distillation. This approach aims to improve LLMs’ ability to reason about abstract and diverse demands. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In simple terms, this paper helps marketers choose the right people for their products or services by using special AI models that can understand natural language requests. The authors created a new way to make these AI models more effective at understanding complex and varied requests from non-expert marketers. |
Keywords
» Artificial intelligence » Distillation » Multi task » Prompting
