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ITMO University

Academic institutioneurope · ru
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Research library142linked papers
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Selected work

Representative Papers

Against Opacity: Explainable AI and Large Language Models for Effective Digital Advertising

Oct 26, 2023ACM Multimedia

Digital advertising platforms (e.g., Meta Ads) suffer from algorithmic opacity, hindering advertisers’ understanding of audience targeting, pricing mechanisms, and ad relevance—thereby impeding data-driven decision-making. To address this, we propose SODA: the first explainable advertising analytics framework integrating multimodal text-image models with large language models (LLMs). Our method introduces a natural-language–based interactive explanation interface tailored for non-technical marketing professionals, enabling automated competitive ad summarization, attribution analysis, and click-through rate (CTR) prediction. By synergistically combining eXplainable AI (XAI) techniques with natural language generation and understanding, SODA enhances predictive accuracy while delivering actionable, trustworthy AI-assisted insights. Evaluated in real-world deployment scenarios, SODA significantly improves interpretability without compromising performance, empowering marketers to make informed, auditable decisions grounded in transparent model reasoning.

12 citationsRead paper

Vikhr: Constructing a State-of-the-art Bilingual Open-Source Instruction-Following Large Language Model for Russian

May 22, 2024MRL

To address the poor generation quality and low computational efficiency of existing large language models for Russian, this paper introduces Vikhr—the first high-performance, bilingual, open-source instruction-following model natively optimized for Russian. Methodologically, Vikhr employs full-parameter continual pretraining followed by supervised instruction fine-tuning, deliberately avoiding parameter-efficient adaptations such as LoRA to achieve vocabulary-level native Russian support. Built upon the Mistral architecture, it features a custom Russian–English tokenizer, alongside substantial expansion of high-quality Russian instruction data and pretraining corpora, enhanced by multi-stage data cleaning and synthetic data generation. Experiments demonstrate that Vikhr establishes new state-of-the-art results among open-source models on multiple Russian-language benchmarks, with several metrics surpassing those of proprietary commercial models. All model weights, datasets, and training code are publicly released.

4 citationsRead paper
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