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  1. ; ; ; ; ( , International Conference on Machine Learning (ICML))
    As the number of large language models (LLMs) released to the public grows, there is a pressing need to understand the safety implications associated with these models learning from third-party custom finetuning data. We explore the behavior of LLMs finetuned on noisy custom data containing unsafe content, represented by datasets that contain biases, toxicity, and harmfulness, finding that while aligned LLMs can readily learn this unsafe content, they also tend to forget it more significantly than other examples when subsequently finetuned on safer content. Drawing inspiration from the discrepancies in forgetting, we introduce the “ForgetFilter” algorithm, which filters unsafe data based on how strong the model’s forgetting signal is for that data. We demonstrate that the ForgetFilter algorithm ensures safety in customized finetuning without compromising downstream task performance, unlike sequential safety finetuning. ForgetFilter outperforms alternative strategies like replay and moral self-correction in curbing LLMs’ ability to assimilate unsafe content during custom finetuning, e.g. 75% lower than not applying any safety measures and 62% lower than using self-correction in toxicity score. 
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    Free, publicly-accessible full text available July 21, 2025
  2. ; ; ( , Bioinformatics)
    Abstract Motivation

    Spatially resolved single-cell transcriptomics have provided unprecedented insights into gene expression in situ, particularly in the context of cell interactions or organization of tissues. However, current technologies for profiling spatial gene expression at single-cell resolution are generally limited to the measurement of a small number of genes. To address this limitation, several algorithms have been developed to impute or predict the expression of additional genes that were not present in the measured gene panel. Current algorithms do not leverage the rich spatial and gene relational information in spatial transcriptomics. To improve spatial gene expression predictions, we introduce Spatial Propagation and Reinforcement of Imputed Transcript Expression (SPRITE) as a meta-algorithm that processes predictions obtained from existing methods by propagating information across gene correlation networks and spatial neighborhood graphs.

     Results

    SPRITE improves spatial gene expression predictions across multiple spatial transcriptomics datasets. Furthermore, SPRITE predicted spatial gene expression leads to improved clustering, visualization, and classification of cells. SPRITE can be used in spatial transcriptomics data analysis to improve inferences based on predicted gene expression.

     Availability and implementation

    The SPRITE software package is available at https://github.com/sunericd/SPRITE. Code for generating experiments and analyses in the manuscript is available at https://github.com/sunericd/sprite-figures-and-analyses.

     
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  3. ; ; ; ; ; ; ; ( , Proceedings of 12th International Conference on Learning Representations (ICLR))
    Free, publicly-accessible full text available May 7, 2025
  4. ; ; ; ; ; ; ; ; ; ; et al ( , Nature Medicine)
    Free, publicly-accessible full text available August 7, 2025
  5. ; ; ; ( , 2023 International Conference on Machine Learning)
    Accepted Manuscript Full Text Available
  6. ; ; ( , The International Conference on Learning Representations (ICLR) 2023)
    Accepted Manuscript Full Text Available
  7. ; ; ( , Proceedings of The 26th International Conference on Artificial Intelligence and Statistics)
    Accepted Manuscript Full Text Available
  8. ; ; ; ; ; ( , Proceedings of The 26th International Conference on Artificial Intelligence and Statistics)
    Accepted Manuscript Full Text Available
  9. ; ; ; ; ; ; ( , International Conference on Learning Representations 2023)
    Accepted Manuscript Full Text Available
  10. ; ; ; ; ( , Advances in neural information processing systems)
    Accepted Manuscript Full Text Available