Optimizing Reasoning Engines Using TF-IDF Vectorization Approach for Enhanced Toxicity Classification

doi:10.1007/978-981-96-6294-4_10

Optimizing Reasoning Engines Using TF-IDF Vectorization Approach for Enhanced Toxicity Classification

2026-01-01

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DOI：10.1007/978-981-96-6294-4_10

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In the rapidly evolving field of information retrieval, enhancing the quality and safety of online search results is crucial. This research focused on the integration of Generative AI into online search engines, emphasizing toxicity classification to improve the response quality of reasoning engines through toxicity classification. By bridging traditional search engines with advanced reasoning engines, the study demonstrated the potential of generative models to enhance search quality and safety. Using TF-IDF vectorization and evaluating models like Bagging Classifier, AdaBoost Classifier, XGB Classifier, and Radom Forest Classifier, the research achieved high performance, with BaggingClassifier leading with 95% accuracy and an Fl -score of 0.94. The future work focuses on implementation of Federated Learning and models like BERT and mBERT to encompass real time data handling, multiple language models and techniques like back translation for data augmentation. The study underscores the improvement of generative AI in creating more intelligent, fair, and user-centric search experiences by effectively identifying and filtering toxic content.

Keywords:

Generative AI

Online Search Engines

Toxic Content Filtering

TF-IDF Vectorization

BaggingClassifier

Optimizing Reasoning Engines Using TF-IDF Vectorization Approach for Enhanced Toxicity Classification

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