Personalized Large Language Models through Parameter Efficient Fine-Tuning Techniques
Marco Braga · 2024
Personalization of the search experience according to the users and their context is an important topic in Information Retrieval (IR), studied by the research community for a long time. The IR field has witnessed a transformation with the recent availability of pre-trained Large Language Models. Typically, personalization requires the model to incorporate user-specific information, through the definition of an appropriate prompting or injecting user knowledge into the model and then fine-tuning it. However, using prompting, we do not know where and how much the model is personalizing the output. Furthermore, fine-tuning such systems is computationally expensive: since they are characterized by billions of parameters, the fine-tuning process has introduced profound computational challenges. For these reasons, we propose a novel approach that combines personalization and Parameter Efficient Fine-Tuning methods.