Summary of User-llm: Efficient Llm Contextualization with User Embeddings, by Lin Ning et al.
User-LLM: Efficient LLM Contextualization with User Embeddings
by Lin Ning, Luyang Liu, Jiaxing Wu, Neo Wu, Devora Berlowitz, Sushant Prakash, Bradley Green, Shawn O’Banion, Jun Xie
First submitted to arxiv on: 21 Feb 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 The paper proposes User-LLM, a framework that integrates large language models (LLMs) with user timeline data by leveraging user embeddings. These embeddings are generated using self-supervised learning on diverse user interactions, capturing latent user behaviors and interests as they evolve over time. The framework combines these user embeddings with LLMs through cross-attention, enabling the models to adapt their responses based on a user’s past actions and preferences. This approach aims to improve the effectiveness of incorporating complex and noisy user timeline data into LLMs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine trying to have a conversation with someone who knows you very well – like your best friend or family member. You want them to understand what you’re saying based on things you’ve done in the past, right? This paper talks about how to make computer programs, called large language models (LLMs), do something similar. They want LLMs to be able to have better conversations with users by understanding their history and preferences. The authors propose a new way of doing this by creating “user embeddings” that capture what people like and dislike over time. This allows the LLMs to adapt their responses based on what they know about each user. |
Keywords
* Artificial intelligence * Cross attention * Self supervised
