Publications

Reflections on NeurIPS 2025: Advancing Evaluation and Continual Learning in AI

Published in Labelbox Blog, 2025

A Labelbox Research team reflection on NeurIPS 2025, covering rigorous evaluation and benchmarking of AI capabilities (data contamination, benchmark faithfulness) and the rise of agentic continual learning via reinforcement learning.

Recommended citation: Golchin, S., Modi, S., Tytarenko, S., Abdibayev, A., & Wetter, M. (2025). "Reflections on NeurIPS 2025: Advancing Evaluation and Continual Learning in AI." Labelbox Blog. https://labelbox.com/blog/reflections-on-neurips-2025-advancing-evaluation-and-continual-learning-in-ai/

Is context attribution all you need to attain generalizability in non-fine-tuned transformer? A Framework for Fake News Detection in Cross dataset Evaluation Settings

Published in ICLR Reliable and Responsible Foundation Models Workshop, 2024

We propose a novel method of context attribution for the transformer model that proves to be more efficient and generalizable. We show that in an example of a fake news detection task, utilizing three distinct datasets and outperforming the baseline model in both the same dataset and cross-dataset zero-shot test.

Recommended citation: TBD http://stepantita.github.io/files/CAM_Framework.pdf

Breaking Free Transformer Models: Task-specific Context Attribution Promises Improved Generalizability Without Fine-tuning Pre-trained LLMs

Published in AAAI Responsible Language Model (ReLM) Workshop, 2024

Best Paper Award Spotlight Presentation AGI Leap Summit 2024 People's Choice — Fordham 3MT 2024

In this paper, we present a framework that allows for maintaining generalizability, and enhances the performance on the downstream task by utilizing task-specific context attribution

Recommended citation: Tytarenko, S. & Amin, M. R. (2024). "Breaking Free Transformer Models: Task-specific Context Attribution Promises Improved Generalizability Without Fine-tuning Pre-trained LLMs." AAAI Responsible Language Model (ReLM) Workshop, 2024. arXiv:2401.16638. http://stepantita.github.io/files/SpaceModel.pdf

https://arxiv.org/abs/2401.16638

Forecasting Bitcoin Price Trends: Leveraging Natural Language Processing and Bing GPT Data Augmentation for Enhanced Predictive Insights

Published in Unpublished, 2022

This study ex- plores the application of Natural Language Processing (NLP) techniques, specifically leveraging transformer models, to predict the impact of news articles on Bitcoin prices

Recommended citation: Tytarenko, S., & Lefebo, K. T. (2023). "Forecasting Bitcoin Price Trends: Leveraging Natural Language Processing and Bing GPT Data Augmentation for Enhanced Predictive Insights." Fordham Graduate School of Arts and Sciences, New York, USA. Email: [stepantita@fordham.edu, klefebo@fordham.edu]. http://stepantita.github.io/files/CryptoBERT.pdf

Ukrainian News Corpus As Text Classification Benchmark

Published in ICTERI Conference, 2022

In this paper we describe a framework for simple classification dataset creation with minimal labeling effort. We create a dataset for Ukrainian news classification and compare several pretrained models for Ukrainian language in different training settings.

Recommended citation: Panchenko, D., Maksymenko, D., Turuta, O., Luzan, M., Tytarenko, S., Turuta, O. (2022). Ukrainian News Corpus as Text Classification Benchmark. In: Ignatenko, O., et al. ICTERI 2021 Workshops. ICTERI 2021. Communications in Computer and Information Science, vol 1635. Springer, Cham. https://doi.org/10.1007/978-3-031-14841-5_37 http://stepantita.github.io/files/NewsClassificationBenchmark.pdf