Personalization is essential in many language tasks, as users with similar needs may prefer different results based on their personal preferences. Traditional methods involve fine-tuning language models for each user, which is resource-intensive. A more practical approach uses retrieval-based systems to personalize results by referring to a user's previous texts. However, this method may not capture a user's overall style and may disrupt the continuity of personalized results. A better solution integrates the user's holistic style into language models without modifying their structure, allowing for personalized results without extensive retraining or computational resources.
Researchers from Renmin University of China and Baidu Inc. introduced a new personalized language model, PPlug. It improves personalization by using a user embedding module that creates a user-specific embedding based on all of their historical interactions. This embedding is attached to the input for the language model to reference, allowing it to generate personalized output without modifying its parameters. Extensive testing on the LaMP benchmark shows that PPlug significantly outperforms existing approaches, achieving improvements ranging from 1.4% to 35.8%. The model efficiently captures users’ holistic behavioral patterns for better personalized language generation.
Recent advances in LLMs have led to personalized approaches to meet the preferences of each user. These methods fall mainly into two categories: personalized, fine-tuned, and retrieval-based LLMs. Fine-tuned models, such as OPPU, fine-tune parameters for each user, but are computationally expensive. To address this issue, parameter-efficient fine-tuning (PEFT) methods, such as LoRA, are employed to optimize efficiency. In contrast, retrieval-based methods leverage the user's history by retrieving relevant documents to guide the LLM results without modifying the model. However, these models face limitations with long user histories due to input length restrictions.
The PPlug model personalizes LLMs by incorporating user-specific embeddings derived from historical behaviors, which guides the fixed LLMs in generating personalized output. The model employs a user behavior encoder to convert each user interaction into vectors, which are then aggregated based on relevance to current inputs via an attention mechanism. Unlike fine-tuned models, PPlug works as a plug-and-play system, reducing computational costs and avoiding parameter tuning for each user. PPlug evaluates all user behaviors against retrieval-based models, providing a comprehensive representation of user preferences for more accurate personalization.
The researchers evaluated their PPlug model using the public LaMP benchmark, which includes six personalization tasks: quote identification, movie tagging, product rating, news headline generation, academic title creation, and tweet paraphrasing. They measured performance with metrics such as accuracy, F1 score, MAE, RMSE, and ROUGE scores. Using FlanT5-XXL and BGE-based encoders, PPlug consistently outperformed baseline methods, including non-personalized and retrieval-based models, achieving improvements ranging from 1.4% to 35.8%. Ablation studies showed that incorporating all user histories and instruction embeddings improves performance. Furthermore, combining PPlug with retrieval strategies further improved the results, demonstrating its effectiveness in capturing full user preferences.
In conclusion, PPlug uses a lightweight, out-of-the-box user embedding module to encode and aggregate a user’s historical behaviors into a single personal embedding, which guides LLMs to generate personalized results. Unlike existing retrieval-based methods, which may fail to capture a user’s general linguistic patterns, PPlug creates a single embedding that takes the input into account to represent a user’s overall style. Experiments on the LaMP benchmark show that PPlug significantly outperforms current personalization methods, achieving more personalized results without requiring extensive model fine-tuning.
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Sana Hassan, a consulting intern at Marktechpost and a dual degree student at IIT Madras, is passionate about applying technology and ai to address real-world challenges. With a keen interest in solving practical problems, she brings a fresh perspective to the intersection of ai and real-life solutions.
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