Summary of Label-guided Prompt For Multi-label Few-shot Aspect Category Detection, by Chaofeng Guan et al.
Label-Guided Prompt for Multi-label Few-shot Aspect Category Detection
by ChaoFeng Guan, YaoHui Zhu, Yu Bai, LingYun Wang
First submitted to arxiv on: 30 Jul 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 The paper proposes a novel approach to multi-label few-shot aspect category detection, which involves representing sentences and categories through label-guided prompts. This method combines contextual and semantic information from sentences to create prompt representations, while also introducing labels into prompts to obtain category descriptions using a large language model. The resulting prototypes are designed to be discriminative and outperform current state-of-the-art methods on two public datasets with an improvement of 3.86% to 4.75% in the Macro-F1 score. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper solves a tricky problem by finding ways to identify multiple aspect categories from short sentences with limited training data. Most approaches try to pick important words, but this can lead to bad results because many words aren’t related to the categories. Instead, the authors suggest using special prompts that combine important information to represent both sentences and categories. This helps create better prototypes that are easy to tell apart. The new method does a lot better than current best methods on two big datasets. |
Keywords
» Artificial intelligence » F1 score » Few shot » Large language model » Prompt
