Summary of User Embedding Model For Personalized Language Prompting, by Sumanth Doddapaneni et al.
User Embedding Model for Personalized Language Prompting
by Sumanth Doddapaneni, Krishna Sayana, Ambarish Jash, Sukhdeep Sodhi, Dima Kuzmin
First submitted to arxiv on: 10 Jan 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Information Retrieval (cs.IR); 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 This study focuses on improving recommendation systems by modeling long user histories. To achieve this, researchers introduce a User Embedding Module (UEM) that efficiently processes user history in free-form text and represents it as an embedding, serving as a soft prompt for language models. The UEM is shown to be superior to conventional text-based prompting methods in handling longer histories, leading to significant improvements in predictive performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study helps make recommendation systems better by understanding people’s changing preferences over time. To do this, the researchers created a new way to represent user history as an “embedding” that can be used to guide language models. This approach is shown to work really well for longer histories and makes recommendations more accurate. |
Keywords
* Artificial intelligence * Embedding * Prompt * Prompting
