Summary of Persoma: Personalized Soft Prompt Adapter Architecture For Personalized Language Prompting, by Liam Hebert et al.
PERSOMA: PERsonalized SOft ProMpt Adapter Architecture for Personalized Language Prompting
by Liam Hebert, Krishna Sayana, Ambarish Jash, Alexandros Karatzoglou, Sukhdeep Sodhi, Sumanth Doddapaneni, Yanli Cai, Dima Kuzmin
First submitted to arxiv on: 2 Aug 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Information Retrieval (cs.IR)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 proposed Personalized Soft Prompt Adapter architecture, PERSOMA, aims to build accurate and personalized natural language systems that adapt to evolving user preferences by understanding nuances of extensive interaction history. Unlike previous methods for large language models, PERSOMA efficiently captures user history through resampling and compressing interactions as free-form text into expressive soft prompt embeddings. The approach is validated by evaluating various adapter architectures, sampling strategies, parameter-efficient tuning techniques like LoRA, and other personalization methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary PERSOMA helps build better natural language systems that understand what people like and want. It does this by taking lots of information about how someone interacts with a system and turning it into special prompts that large language models can understand. This makes the system more personalized and accurate. The researchers tested different ways to do this and found that PERSOMA works better than other methods. |
Keywords
» Artificial intelligence » Lora » Parameter efficient » Prompt
