User Embedding Model for Personalized Language Prompting (2401.04858v1)

Abstract: Modeling long histories plays a pivotal role in enhancing recommendation systems, allowing to capture user's evolving preferences, resulting in more precise and personalized recommendations. In this study we tackle the challenges of modeling long user histories for preference understanding in natural language. Specifically, we introduce a new User Embedding Module (UEM) that efficiently processes user history in free-form text by compressing and representing them as embeddings, to use them as soft prompts to a LM. Our experiments demonstrate the superior capability of this approach in handling significantly longer histories compared to conventional text based prompting methods, yielding substantial improvements in predictive performance. The main contribution of this research is to demonstrate the ability to bias LLMs with user signals represented as embeddings.

• The paper presents a novel User Embedding Module (UEM) that captures extended user histories as compact embeddings.
• It leverages personalized soft prompts to tailor language model outputs without high computational costs.
• Empirical results demonstrate up to a 0.25 improvement in F1 score over baseline methods in text-to-text tasks.

Introduction to User Embedding for LLMs

LLMs (LMs) have remarkably transformed the way artificial intelligence systems understand and generate human language. Their applications span from chatbots and recommendation systems to more complex tasks, such as reasoning and creativity assistance. These models have largely benefited from the mass of data available on the internet, which has taught them to adapt rapidly and generalize across a wide range of contexts and questions. However, personalization has remained a nuanced challenge, particularly when it comes to understanding and integrating long user histories into the responses and suggestions these models provide.

Advancements in Personalized Language Prompting

The primary innovation presented in the discussed research is the User Embedding Module (UEM), a technique designed to capture and represent extended user interaction histories as compact embeddings. These embeddings can be used as personalized soft prompts for LMs, thereby influencing the model's outputs to align better with an individual's preferences and behavior. This approach is markedly more efficient than previous methods, which often involved sifting through shorter segments of user history or various prompt rewriting techniques. In contrast, the UEM is capable of capturing an entire user's history without the prohibitive computational costs typically associated with larger data sequences.

Empirical Findings and Methodology

The research deployed the UEM within a text-to-text generation framework, where tasks are framed as language generation conditioned on input. Their empirical results highlighted the efficiency of UEM, showing a considerable improvement in predictive performance, with up to a 0.25 improvement in F1 scores over baseline methods for handling user history. The user history was represented as various embeddings covering aspects like movie titles, genres, ratings, and descriptions, subsequently processed through the UEM. This enables the LM, coupled with UEM, to handle user histories with more depth — up to 50 items in this experiment — significantly surpassing the limitations of traditional text-based LMs.

Looking Forward

The findings of this paper have immediate implications for enhancing the personalization of LMs in various applications, as seen in performance improvements when the UEM is implemented. In the future, exploring different ways to fine-tune such LMs using parameter-efficient techniques or extending these methods to multimodal signals holds potential for further advancements.

In conclusion, this research successfully takes a stride towards integrating longer user histories into LMs by representing these histories with an embedding-based approach. The approach not only enhances the performance of LMs in predicting user preferences but does so with computational efficiency, raising the possibility for more personalized, context-aware AI applications in the near future.