Summary of Step-back Profiling: Distilling User History For Personalized Scientific Writing, by Xiangru Tang et al.
Step-Back Profiling: Distilling User History for Personalized Scientific Writing
by Xiangru Tang, Xingyao Zhang, Yanjun Shao, Jie Wu, Yilun Zhao, Arman Cohan, Ming Gong, Dongmei Zhang, Mark Gerstein
First submitted to arxiv on: 20 Jun 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 proposed STEP-BACK PROFILING approach personalizes large language models (LLMs) by distilling user history into concise profiles, capturing essential traits and preferences. This is demonstrated through the Personalized Scientific Writing (PSW) dataset, which requires LLMs to write scientific papers given diverse academic backgrounds. The results show that STEP-BACK PROFILING outperforms baselines by up to 3.6 points on the general personalization benchmark (LaMP), including seven tasks. Ablation studies validate the contributions of different components and provide insights into task definition. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models are great at many natural language processing tasks, but they struggle to create personalized content for individuals in real-life scenarios like writing scientific papers. To help with this challenge, a new approach called STEP-BACK PROFILING was developed. This method creates profiles that capture important traits and preferences of users. These profiles were tested on a special dataset called Personalized Scientific Writing (PSW). The results showed that STEP-BACK PROFILING worked well for collaborative writing. |
Keywords
* Artificial intelligence * Natural language processing
