Summary of Plug and Play with Prompts: a Prompt Tuning Approach For Controlling Text Generation, by Rohan Deepak Ajwani et al.
Plug and Play with Prompts: A Prompt Tuning Approach for Controlling Text Generation
by Rohan Deepak Ajwani, Zining Zhu, Jonathan Rose, Frank Rudzicz
First submitted to arxiv on: 8 Apr 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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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 A novel approach to controlled language generation using Prompt Tuning is introduced in this research paper. Transformer-based Large Language Models (LLMs) are capable of exceptional language generation, but have been challenging to steer with textual prompts, especially with smaller models. To overcome this limitation, prompt embeddings are trained using a small language model as a discriminator. This method offers a data and parameter efficient solution for controlling language model outputs. The proposed approach is evaluated on four datasets: SST-5 and Yelp (sentiment analysis), GYAFC (formality) and JIGSAW (toxic language). The results demonstrate the efficacy of this method in mitigating harmful, toxic, and biased text generated by language models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Language generation using large language models is great, but it’s hard to control what they say. This paper shows a way to make them generate specific things when given prompts. It uses special embeddings to help steer the model’s output. The method works well even with small amounts of training data. The researchers tested it on several datasets and found that it can reduce harmful language generated by these models. |
Keywords
» Artificial intelligence » Language model » Parameter efficient » Prompt » Transformer
