Year: 2026
Type: conference-paper
Abstract: Vietnam ranks among the top countries in terms of both internet traffic and online toxicity.As a result, implementing embedding models for recommendation and content control duties in applications is crucial.However, a lack of large-scale test datasets, both in volume and task diversity, makes it tricky for scientists to effectively evaluate AI models before deploying them in real-world, large-scale projects.To solve this important problem, we introduce a text embeddings.In our analysis, we find that bigger and more complex models using Rotary Positional Embedding outperform those using Absolute Positional Embedding in embedding tasks.
Authors: Loc Pham, Tung Luu, Thu Vo, Minh Nguyen, Viet Hoang
Institution: Vietnam National University Ho Chi Minh City
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W7140132489