Enhancing Personalized Text Generation with Neural Bandits and White-box Language Models

Основні поняття

A novel online method that employs neural bandit algorithms to dynamically optimize soft instruction embeddings based on user feedback, enhancing the personalization of open-ended text generation by white-box large language models.

Анотація

This study introduces an innovative approach to personalized text generation using white-box large language models (LLMs). The key insights are:

Personalization in text generation is crucial for user engagement and satisfaction, but developing a unique LLM for each user is impractical due to resource constraints and data privacy concerns.

The authors propose using lightweight models capable of online learning, which can dynamically adjust their output based on continuous user feedback. This approach circumvents the need for a bespoke model for each user and encourages alignment of the generated content to individual preferences over time.

The authors adopt neural bandit algorithms, specifically NeuralUCB and NeuralTS, to directly optimize the soft token embeddings (representing contextual factors) through user feedback. This method refines the personalization process of text generation and contributes to the broader application of adaptive algorithms in creating content that closely reflects individual user preferences.

Experiments on the LaMP benchmark dataset demonstrate significant performance improvements in personalized news headline generation, personalized scholarly title generation, and personalized tweet paraphrasing tasks, with NeuralTS achieving up to a 62.9% increase in ROUGE scores and up to a 2.76% increase in LLM-agent evaluation compared to the baseline.

The authors acknowledge the limitations of the study, including the need for more diverse tasks, human evaluations, and comparisons to a wider range of adaptive optimization algorithms. They also discuss the ethical considerations around privacy, security, fairness, and the broader social impacts of deploying personalized generative models.

Статистика

The proposed framework achieves up to a 62.9% improvement in average ROUGE-1/L scores for personalized news headline generation compared to the random baseline.
In personalized scholarly title generation, the framework outperforms the baseline by approximately 53.3% in average ROUGE-1/L scores.
For personalized tweet paraphrasing, the framework leads to a 34.7% increase in average ROUGE-1/L scores over the baseline.
The NeuralUCB algorithm achieves an average improvement of 2.8% in LLM-agent evaluation for personalized news headline generation, while NeuralTS sees an improvement of around 1%.

Цитати

"The advent of personalized content generation by LLMs presents a novel challenge: how to efficiently adapt text to meet individual preferences without the unsustainable demand of creating a unique model for each user."
"By leveraging past interactions to balance the trade-off between exploring new actions and exploiting known ones, these [neural bandit] algorithms can accurately predict and enhance personalized outcomes."
"Importantly, this adaptive process is poised to unlock long-term rewards stemming from personalization, encompassing not just explicit preferences expressed by users but also responding to favorable actions."

Ключові висновки, отримані з

Online Personalizing White-box LLMs Generation with Neural Bandits

by Zekai Chen,W... о arxiv.org 04-26-2024

https://arxiv.org/pdf/2404.16115.pdf

Глибші Запити

How can the proposed framework be extended to handle more complex and diverse user profiles, such as those with evolving preferences or multiple personas?

The proposed framework can be extended to handle more complex and diverse user profiles by incorporating adaptive learning mechanisms that can adjust to evolving preferences or multiple personas. One approach could involve integrating reinforcement learning techniques to allow the system to learn and adapt based on user feedback over time. By continuously updating the soft prompts based on user interactions, the model can better capture the nuances of individual preferences and adapt to changing user profiles. Additionally, incorporating a hierarchical structure in the model could help differentiate between different personas within the same user profile, enabling more personalized and targeted content generation. By leveraging a combination of neural bandit algorithms and reinforcement learning, the framework can dynamically optimize soft prompts to cater to a wide range of user profiles with varying preferences and personas.

What are the potential risks and mitigation strategies for using personalized text generation models in sensitive domains like healthcare or finance?

Using personalized text generation models in sensitive domains like healthcare or finance poses several risks, including privacy concerns, ethical implications, and the potential for generating misleading or harmful content. To mitigate these risks, several strategies can be implemented:

Data Privacy: Implement strict data privacy measures to ensure that sensitive information is protected and anonymized before being used for training the models. Adhere to data protection regulations such as GDPR to safeguard user data.

Ethical Guidelines: Establish clear ethical guidelines for content generation in sensitive domains, outlining what is considered appropriate and ethical. Regular audits and reviews can help ensure compliance with these guidelines.

Bias Detection and Mitigation: Implement bias detection mechanisms to identify and mitigate any biases present in the generated content, especially in domains where fairness and accuracy are critical, such as healthcare or finance.

Human Oversight: Incorporate human oversight in the content generation process to review and validate the output, especially in critical scenarios where inaccuracies or errors could have serious consequences.

Transparency and Accountability: Maintain transparency in how the models operate and make decisions, providing explanations for the generated content. Establish accountability measures to address any issues or errors that may arise.

How can the insights from this study on optimizing soft prompts be applied to other areas of language model personalization, such as few-shot learning or multi-task adaptation?

The insights from optimizing soft prompts can be applied to other areas of language model personalization, such as few-shot learning or multi-task adaptation, in the following ways:

Few-Shot Learning: By fine-tuning the soft prompts based on user feedback, models can be adapted to perform well in few-shot learning scenarios where limited training data is available. The optimized prompts can help the model generalize better and make accurate predictions with minimal training examples.

Multi-Task Adaptation: Optimizing soft prompts can enhance the model's ability to adapt to multiple tasks simultaneously. By dynamically adjusting the prompts based on the specific requirements of each task, the model can efficiently switch between tasks and perform effectively across a range of domains.

Transfer Learning: The optimized soft prompts can facilitate transfer learning by capturing the essential features of different tasks or domains. This enables the model to leverage knowledge from one task to improve performance on related tasks, enhancing overall efficiency and effectiveness in multi-task settings.

By leveraging the insights gained from optimizing soft prompts, language models can be tailored to excel in diverse scenarios, including few-shot learning, multi-task adaptation, and transfer learning, thereby improving their versatility and performance across various applications.