This study explores the enhancement of deductive reasoning capabilities in Large Language Models (LLMs) through a strategic dual-agent framework. In this framework, one agent acts as a questioner and another as an answerer, with both employing advanced linguistic and logical processing to optimize information exchange. Utilizing a structured environment that limits query opportunities, our approach emphasizes the development of LLMs that can efficiently generate and interpret questions to deduce hidden information effectively. The models, which incorporate self-defined agents with a combination of pretraining and llama-3-8b enhancements, demonstrate a remarkable ability to navigate the complexities of logical deduction. Performance evaluations, based on a series of simulated interactions, illustrate the agents' improved precision and strategic acumen in narrowing down possibilities through targeted inquiries. These findings underscore the potential of LLMs in tasks requiring intricate reasoning and collaboration, marking a significant step towards more intelligent and autonomous systems.

Credit scoring is vital in the financial industry,assessing the risk of lending to credit card applicants. Traditionalcredit scoring methods face challenges with large datasets anddata imbalance between creditworthy and non-creditworthy applicants.This paper introduces an advanced machine learningand deep learning framework to improve the accuracy andreliability of credit card approval predictions. We utilized extensivedatasets of user application records and credit history,implementing a comprehensive preprocessing strategy, featureengineering, and model integration. Our methodology combinesneural networks with an ensemble of base models, includinglogistic regression, support vector machines, k-nearest neighbors,decision trees, random forests, and gradient boosting. The ensembleapproach addresses data imbalance using Synthetic MinorityOver-sampling Technique (SMOTE) and mitigates overfittingrisks. Experimental results show that our integrated modelsurpasses traditional single-model approaches in precision, recall,F1-score, AUC, and Kappa, providing a robust and scalablesolution for credit card approval predictions. This researchunderscores the potential of advanced machine learning techniquesto transform credit risk assessment and financial decisionmaking.

In the rapidly evolving landscape of personalized recommendation systems, accurately predicting user purchase behavior remains a critical challenge. This study presents a novel hybrid model combining LightGBM, DeepFM, and Deep Interest Network (DIN) architectures to enhance the prediction accuracy for the Elo payment dataset. Our approach leverages advanced feature engineering, including clustering and temporal feature extraction, coupled with robust training strategies such as adversarial weight perturbation (AWP) and pseudo-labeling. By integrating these techniques, we achieve superior performance compared to existing models, as measured by AUC (Area Under the Curve) and NDCG (Normalized Discounted Cumulative Gain) metrics. Our model demonstrates a significant improvement in prediction precision, particularly for high-ranking predictions, thereby offering a comprehensive solution to the complexities of user behavior modeling in payment datasets.

Credit scoring is vital in the financial industry, assessing the risk of lending to credit card applicants. Traditional credit scoring methods face challenges with large datasets and data imbalance between creditworthy and non-creditworthy applicants. This paper introduces an advanced machine learning and deep learning framework to improve the accuracy and reliability of credit card approval predictions. We utilized extensive datasets of user application records and credit history, implementing a comprehensive preprocessing strategy, feature engineering, and model integration. Our methodology combines neural networks with an ensemble of base models, including logistic regression, support vector machines, k-nearest neighbors, decision trees, random forests, and gradient boosting. The ensemble approach addresses data imbalance using Synthetic Minority Over-sampling Technique (SMOTE) and mitigates overfitting risks. Experimental results show that our integrated model surpasses traditional single-model approaches in precision, recall, F1-score, AUC, and Kappa, providing a robust and scalable solution for credit card approval predictions. This research underscores the potential of advanced machine learning techniques to transform credit risk assessment and financial decision-making.

The task of matching educational content to specific topics within curricula is crucial for enhancing learning outcomes and ensuring that students receive relevant and coherent learning experiences. This research seeks to develop a precise and efficient model, leveraging a dataset that spans various languages and numerous STEM fields. Our proposed solution integrates a Transformer model with InfoNCE Loss and model distillation techniques to effectively address noise from related content and improve matching accuracy. By incorporating advanced deep learning methods, the model is designed to handle the complexity and variability of educational datasets, outperforming existing models in terms of accuracy and efficiency. This approach provides a robust framework for educational content alignment, significantly benefiting educational practitioners by streamlining the content matching process and enhancing the quality of education delivered to students.