We present BenCzechMark (BCM), the first comprehensive Czech language benchmark designed for large language models, offering diverse tasks, multiple task formats, and multiple evaluation metrics. Its scoring system is grounded in statistical significance theory and uses aggregation across tasks inspired by social preference theory. Our benchmark encompasses 50 challenging tasks, with corresponding test datasets, primarily in native Czech, with 11 newly collected ones. These tasks span 8 categories and cover diverse domains, including historical Czech news, essays from pupils or language learners, and spoken word. Furthermore, we collect and clean BUT-Large Czech Collection, the largest publicly available clean Czech language corpus, and use it for (i) contamination analysis, (ii) continuous pretraining of the first Czech-centric 7B language model, with Czech-specific tokenization. We use our model as a baseline for comparison with publicly available multilingual models. Lastly, we release and maintain a leaderboard, with existing 44 model submissions, where new model submissions can be made at https://huggingface.co/spaces/CZLC/BenCzechMark.

Data imbalance is a fundamental challenge in applying language models to biomedical applications, particularly in ICD code prediction tasks where label and demographic distributions are uneven. While state-of-the-art language models have been increasingly adopted in biomedical tasks, few studies have systematically examined how data imbalance affects model performance and fairness across demographic groups. This study fills the gap by statistically probing the relationship between data imbalance and model performance in ICD code prediction. We analyze imbalances in a standard benchmark data across gender, age, ethnicity, and social determinants of health by state-of-the-art biomedical language models. By deploying diverse performance metrics and statistical analyses, we explore the influence of data imbalance on performance variations and demographic fairness. Our study shows that data imbalance significantly impacts model performance and fairness, but feature similarity to the majority class may be a more critical factor. We believe this study provides valuable insights for developing more equitable and robust language models in healthcare applications.

As the use of machine learning models has increased, numerous studies have aimed to enhance fairness. However, research on the intersection of fairness and explainability remains insufficient, leading to potential issues in gaining the trust of actual users. Here, we propose a novel module that constructs a fair latent space, enabling faithful explanation while ensuring fairness. The fair latent space is constructed by disentangling and redistributing labels and sensitive attributes, allowing the generation of counterfactual explanations for each type of information. Our module is attached to a pretrained generative model, transforming its biased latent space into a fair latent space. Additionally, since only the module needs to be trained, there are advantages in terms of time and cost savings, without the need to train the entire generative model. We validate the fair latent space with various fairness metrics and demonstrate that our approach can effectively provide explanations for biased decisions and assurances of fairness.

Cross-Project Defect Prediction (CPDP) poses a non-trivial challenge to construct a reliable defect predictor by leveraging data from other projects, particularly when data owners are concerned about data privacy. In recent years, Federated Learning (FL) has become an emerging paradigm to guarantee privacy information by collaborative training a global model among multiple parties without sharing raw data. While the direct application of FL to the CPDP task offers a promising solution to address privacy concerns, the data heterogeneity arising from proprietary projects across different companies or organizations will bring troubles for model training. In this paper, we study the privacy-preserving cross-project defect prediction with data heterogeneity under the federated learning framework. To address this problem, we propose a novel knowledge enhancement approach named FedDP with two simple but effective solutions: 1. Local Heterogeneity Awareness and 2. Global Knowledge Distillation. Specifically, we employ open-source project data as the distillation dataset and optimize the global model with the heterogeneity-aware local model ensemble via knowledge distillation. Experimental results on 19 projects from two datasets demonstrate that our method significantly outperforms baselines.

The rapid growth of social media platforms has raised significant concerns regarding online content toxicity. When Large Language Models (LLMs) are used for toxicity detection, two key challenges emerge: 1) the absence of domain-specific toxic knowledge leads to false negatives; 2) the excessive sensitivity of LLMs to toxic speech results in false positives, limiting freedom of speech. To address these issues, we propose a novel method called MetaTox, leveraging graph search on a meta-toxic knowledge graph to enhance hatred and toxicity detection. First, we construct a comprehensive meta-toxic knowledge graph by utilizing LLMs to extract toxic information through a three-step pipeline, with toxic benchmark datasets serving as corpora. Second, we query the graph via retrieval and ranking processes to supplement accurate, relevant toxic knowledge. Extensive experiments and in-depth case studies across multiple datasets demonstrate that our MetaTox significantly decreases the false positive rate while boosting overall toxicity detection performance. Our code will be available soon.

Machine learning models used in medical applications often face challenges due to the covariate shift, which occurs when there are discrepancies between the distributions of training and target data. This can lead to decreased predictive accuracy, especially with unknown outcomes in the target data. This paper introduces a semi-supervised classification and regression tree (CART) that uses importance weighting to address these distribution discrepancies. Our method improves the predictive performance of the CART model by assigning greater weights to training samples that more accurately represent the target distribution, especially in cases of covariate shift without target outcomes. In addition to CART, we extend this weighted approach to generalized linear model trees and tree ensembles, creating a versatile framework for managing the covariate shift in complex datasets. Through simulation studies and applications to real-world medical data, we demonstrate significant improvements in predictive accuracy. These findings suggest that our weighted approach can enhance reliability in medical applications and other fields where the covariate shift poses challenges to model performance across various data distributions.

Machine learning continues to grow in popularity in academia, in industry, and is increasingly used in other fields. However, most of the common metrics used to evaluate even simple binary classification models have shortcomings that are neither immediately obvious nor consistently taught to practitioners. Here we present Interactive Classification Metrics (ICM), an application to visualize and explore the relationships between different evaluation metrics. The user changes the distribution statistics and explores corresponding changes across a suite of evaluation metrics. The interactive, graphical nature of this tool emphasizes the tradeoffs of each metric without the overhead of data wrangling and model training. The goals of this application are: (1) to aid practitioners in the ever-expanding machine learning field to choose the most appropriate evaluation metrics for their classification problem; (2) to promote careful attention to interpretation that is required even in the simplest scenarios like binary classification. Our application is publicly available for free under the MIT license as a Python package on PyPI at https://pypi.org/project/interactive-classification-metrics and on GitHub at https://github.com/davhbrown/interactive_classification_metrics.

Abnormalities in the gastrointestinal tract significantly influence the patient's health and require a timely diagnosis for effective treatment. With such consideration, an effective automatic classification of these abnormalities from a video capsule endoscopy (VCE) frame is crucial for improvement in diagnostic workflows. The work presents the process of developing and evaluating a novel model designed to classify gastrointestinal anomalies from a VCE video frame. Integration of Omni Dimensional Gated Attention (OGA) mechanism and Wavelet transformation techniques into the model's architecture allowed the model to focus on the most critical areas in the endoscopy images, reducing noise and irrelevant features. This is particularly advantageous in capsule endoscopy, where images often contain a high degree of variability in texture and color. Wavelet transformations contributed by efficiently capturing spatial and frequency-domain information, improving feature extraction, especially for detecting subtle features from the VCE frames. Furthermore, the features extracted from the Stationary Wavelet Transform and Discrete Wavelet Transform are concatenated channel-wise to capture multiscale features, which are essential for detecting polyps, ulcerations, and bleeding. This approach improves classification accuracy on imbalanced capsule endoscopy datasets. The proposed model achieved 92.76% and 91.19% as training and validation accuracies respectively. At the same time, Training and Validation losses are 0.2057 and 0.2700. The proposed model achieved a Balanced Accuracy of 94.81%, AUC of 87.49%, F1-score of 91.11%, precision of 91.17%, recall of 91.19% and specificity of 98.44%. Additionally, the model's performance is benchmarked against two base models, VGG16 and ResNet50, demonstrating its enhanced ability to identify and classify a range of gastrointestinal abnormalities accurately.

The dramatic increase in consumption of ultra-processed food has been associated with numerous adverse health effects. Given the public health consequences linked to ultra-processed food consumption, it is highly relevant to build computational models to predict the processing of food products. We created a range of machine learning, deep learning, and NLP models to predict the extent of food processing by integrating the FNDDS dataset of food products and their nutrient profiles with their reported NOVA processing level. Starting with the full nutritional panel of 102 features, we further implemented coarse-graining of features to 65 and 13 nutrients by dropping flavonoids and then by considering the 13-nutrient panel of FDA, respectively. LGBM Classifier and Random Forest emerged as the best model for 102 and 65 nutrients, respectively, with an F1-score of 0.9411 and 0.9345 and MCC of 0.8691 and 0.8543. For the 13-nutrient panel, Gradient Boost achieved the best F1-score of 0.9284 and MCC of 0.8425. We also implemented NLP based models, which exhibited state-of-the-art performance.

With the widespread application of large language models (LLM), concerns about the privacy leakage of model training data have increasingly become a focus. Membership Inference Attacks (MIAs) have emerged as a critical tool for evaluating the privacy risks associated with these models. Although existing attack methods, such as LOSS, Reference-based, min-k, and zlib, perform well in certain scenarios, their effectiveness on large pre-trained language models often approaches random guessing, particularly in the context of large-scale datasets and single-epoch training. To address this issue, this paper proposes a novel ensemble attack method that integrates several existing MIAs techniques (LOSS, Reference-based, min-k, zlib) into an XGBoost-based model to enhance overall attack performance (EM-MIAs). Experimental results demonstrate that the ensemble model significantly improves both AUC-ROC and accuracy compared to individual attack methods across various large language models and datasets. This indicates that by combining the strengths of different methods, we can more effectively identify members of the model's training data, thereby providing a more robust tool for evaluating the privacy risks of LLM. This study offers new directions for further research in the field of LLM privacy protection and underscores the necessity of developing more powerful privacy auditing methods.