# Integrating Summarization and Retrieval for Enhanced Personalization via Large Language Models

**Authors:** Chris Richardson, Yao Zhang, Kellen Gillespie, Sudipta Kar, Arshdeep Singh, Zeynab Raeesy, Omar Zia Khan, Abhinav Sethy  
**arXiv:** [2310.20081](https://arxiv.org/abs/2310.20081)  
**Date:** 2023-10-30  
**Summary page:** https://www.summarizepaper.com/en/paper/2310.20081v1/

## Key points

- Research focus: Leveraging Large Language Models (LLMs) for personalizing user experiences with Natural Language Processing (NLP) systems
- Methodology: Using LLMs to generate abstractive summaries of relevant user information offline
- Retrieval process: Selecting top-k profile entries and combining them with task input and summary for downstream language model fine-tuning
- Experiment results: Superior performance using FlanT5-base and BM25 retrieval algorithm compared to other methods like Contriever in terms of speed and accuracy
- Benefits of approach: Addressing challenges such as information loss, lack of user understanding, and cold-start issues effectively

## Summary

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Our research focuses on leveraging Large Language Models (LLMs) to personalize user experiences with Natural Language Processing (NLP) systems. Personalization is crucial for enhancing the user experience, and we aim to optimize the output of language models by incorporating past user data. To achieve this, we propose a novel approach that involves using LLMs to generate abstractive summaries of relevant user information. This method entails generating these summaries offline using instruction-tuned models such as Vicuna and ChatGPT. These summaries are then stored along with the user data and retrieved at runtime. The retrieval algorithm selects the top-k profile entries and combines them with the task input and summary to provide context for downstream language model fine-tuning. We conducted experiments using the LaMP benchmark dataset, which consists of seven personalization NLP tasks. By utilizing FlanT5-base as our downstream model and the BM25 retrieval algorithm for efficiency, we were able to achieve superior performance compared to other methods like Contriever in terms of speed and accuracy. Our experiments demonstrated that our summary-augmented approach outperformed traditional retrieval augmentation on most tasks in the LaMP benchmark. By combining offline summarization with runtime retrieval, we were able to address challenges such as information loss, lack of user understanding, and cold-start issues effectively. Overall, our research showcases the effectiveness of leveraging LLMs for personalized NLP systems under practical constraints. Our refined approach not only improves performance but also enables real-world systems like voice assistants to benefit from advanced language modeling techniques.