Summary of Aprompt4em: Augmented Prompt Tuning For Generalized Entity Matching, by Yikuan Xia et al.
APrompt4EM: Augmented Prompt Tuning for Generalized Entity Matching
by Yikuan Xia, Jiazun Chen, Xinchi Li, Jun Gao
First submitted to arxiv on: 8 May 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 introduces an augmented prompt tuning framework for Generalized Entity Matching (GEM) in low-resource scenarios. The framework improves upon existing prompt tuning models by incorporating a soft token-based approach and a cost-effective information augmentation strategy leveraging large language models (LLMs). The proposed method outperforms existing methods based on moderate-size PLMs, with an average improvement of 5.24%, and achieves comparable performance to fine-tuned LLMs while using less than 14% of the API fee. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper solves a problem called Generalized Entity Matching (GEM). GEM tries to figure out if two pieces of information are about the same real thing, even if they’re written in different ways. To help with this, researchers have been using language models to learn from prompts – special instructions that tell the model what to do. But these prompts can be tricky to design and make sure they don’t leave out important details. This paper introduces a new way of making these prompts, called augmented prompt tuning, which uses two main ideas: a “soft token” approach that helps guide the language model’s learning, and an information augmentation strategy that makes use of large language models. The results show that this new method works well in low-resource scenarios. |
Keywords
» Artificial intelligence » Language model » Prompt » Token
