Summary of Hydra: Model Factorization Framework For Black-box Llm Personalization, by Yuchen Zhuang et al.
HYDRA: Model Factorization Framework for Black-Box LLM Personalization
by Yuchen Zhuang, Haotian Sun, Yue Yu, Rushi Qiang, Qifan Wang, Chao Zhang, Bo Dai
First submitted to arxiv on: 5 Jun 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 Personalization has become a crucial area of research in modern intelligent systems, focusing on adapting to users’ preferences by mining their behavioral history. Despite the impressive capabilities of black-box large language models (LLMs), their opacity poses significant challenges in aligning generated output with individual expectations. Existing solutions primarily rely on prompt design to incorporate user-specific profiles and behaviors; however, these approaches often struggle to generalize due to their inability to capture shared knowledge among all users. To address this, we propose HYDRA, a model factorization framework that captures both user-specific behavior patterns and shared general knowledge to deliver personalized generation. This is achieved by training a reranker to prioritize historical data and an adapter to align output with individual preferences. Both the reranker and adapter can be decomposed into a base model with multiple user-specific heads, similar to a hydra. Experimental results demonstrate that HYDRA outperforms existing state-of-the-art prompt-based methods by 9.01% on average across five diverse personalization tasks in the LaMP benchmark. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research paper is about making computers better at understanding people’s preferences and habits. Currently, big language models can be very good at generating text based on what they’ve learned from a little bit of data, but they don’t always understand what makes each person unique. The researchers created a new system called HYDRA that helps computers understand individual people by combining their personal experiences with general knowledge. This means that when you use a computer or talk to an AI assistant, it can give you more personalized and relevant information that takes into account your own preferences and habits. |
Keywords
» Artificial intelligence » Prompt
