Democratizing Large Language Models via Personalized Parameter-Efficient Fine-tuning

Personalization in large language models (LLMs) is increasingly important, aiming to align LLM's interactions, content, and recommendations with individual user preferences. Recent advances in LLM personalization have spotlighted effective prompt design, by enriching user queries with non-parametric knowledge through behavior history retrieval and textual profiles. However, these approaches were limited due to a lack of model ownership, resulting in constrained customization and privacy issues. Moreover, they often failed to accurately capture user behavior patterns, especially in cases where user data were complex and dynamic. To address these shortcomings, we introduce One PEFT Per User (OPPU), which employs personalized parameter-efficient fine-tuning (PEFT) modules, to store user-specific behavior patterns and preferences. By plugging in users' personal PEFT parameters, they can own and use their LLMs personally. OPPU integrates parametric user knowledge in the personal PEFT parameters with the non-parametric knowledge acquired through retrieval and profile. This integration adapts individual LLMs to user behavior shifts. Experimental results demonstrate that OPPU significantly outperforms existing prompt-based methods across seven diverse tasks in the LaMP benchmark. Further in-depth studies reveal OPPU's enhanced capabilities in handling user behavior shifts, modeling users at different active levels, maintaining robustness across various user history formats, and displaying versatility with different PEFT methods

Investigation of improving the thermophysical properties and corrosion resistance of RE2SiO5/RE2Si2O7 multiphase silicates by component design with RE doping

In this research, a novel method for regulating components in RE2SiO5/RE2Si2O7 multiphase silicates was developed, combining the benefits of a suitable thermal expansion coefficient (CTE) and outstanding corrosion resistance against calcium–magnesium–alumino–silicate (CMAS). This approach enhanced the overall thermophysical properties. Additionally, the results from the CMAS corrosion resistance test indicated that (Lu1/3Yb1/3Tm1/3)2SiO5/(Lu1/3Yb1/3Tm1/3)2Si2O7 and (Lu1/4Yb1/4Tm1/4Er1/4)2SiO5/(Lu1/4Yb1/4Tm1/4Er1/4)2Si2O7 exhibited exceptional resistance to CMAS penetration, even at temperatures up to 1500 °C. To comprehend the corrosion mechanism of CMAS on these silicates, we introduced a reaction–diffusion model, which involved observing the changes in the interface between the corrosion product layer and the silicate block. This was achieved using electron backscatter diffraction (EBSD). These findings lay a theoretical basis for selecting rare earth elements in RE2SiO5/RE2Si2O7 multiphase silicates based on the radii of different rare earth cations

Machine Learning in Neuroimaging: A New Approach to Understand Acupuncture for Neuroplasticity

The effects of acupuncture facilitating neural plasticity for treating diseases have been identified by clinical and experimental studies. In the last two decades, the application of neuroimaging techniques in acupuncture research provided visualized evidence for acupuncture promoting neuroplasticity. Recently, the integration of machine learning (ML) and neuroimaging techniques becomes a focus in neuroscience and brings a new and promising approach to understand the facilitation of acupuncture on neuroplasticity at the individual level. This review is aimed at providing an overview of this rapidly growing field by introducing the commonly used ML algorithms in neuroimaging studies briefly and analyzing the characteristics of the acupuncture studies based on ML and neuroimaging, so as to provide references for future research

The Specific and Nonspecific Effects of Tai Chi and Its Possible Central Responses: A Protocol of Neuroimaging Study

Tai Chi has been proven to be a safe and effective assistant therapy for healthcare and disease treatment. However, whether the adjuvant therapeutic effect of Tai Chi is general or disease-oriented remains uncertain. This trial focuses on exploring the specific and nonspecific effects of Tai Chi and its potential central responses. The results will deepen our understanding of the characteristics of Tai Chi exercise for adjuvant therapeutic effects and promote its application in the clinic. In this neuroimaging trial, 40 functional constipation (FC) patients and 40 healthy subjects (HS) will be recruited and will receive 10 weeks of Tai Chi exercise. The motor function, respiratory function, stool-related symptoms, quality of life, and emotional state of the participants will be evaluated at the baseline, the 5-week Tai Chi practice, and the end of practice. The potential changes in the heart rate variability and the cerebral function will be recorded by the 24 h dynamic electrocardiogram at the baseline and the functional magnetic resonance imaging at the end of practice. The possible correlations among the clinical variables, the heart rate variability, and the cerebral activity alterations in FC patients and HS will be analyzed. The healthcare and therapeutic effects of Tai Chi exercise might consist of the specific and nonspecific effects. This study provides not only a new perspective for understanding Tai Chi but also a new approach for investigating the mind-body exercise. This trial was registered in the Chinese Clinical Trial Registry (http://www.chictr.org.cn/showproj.aspx?proj=33243) on 28 November 2018 (registration number: ChiCTR1800019781; protocol version number: V1.0). This trial is currently in the stage of recruiting patients. The first patient was included on 1 December 2018. To date, 18 FC patients and 20 HS have been included. Recruitment will be completed in December 2020