5 citations · 5 across the 5 of their papers we have counts for
6 papers
Do Your Own Research: Learning to Forecast by Learning to Search
Yusuf Afifi, Artur Kiulian, Anton Polishko +3
Outcome-based reinforcement learning can train language models to forecast real-world events, but prior forecasting work either freezes research context before training or deploys…
Belief Propagation in LLM World Models: Measuring Strategic Information Bias with Prediction Markets
Mykola Khandoga, Yevhen Kostiuk, Anton Polishko +4
Every information ecosystem produces beliefs that shape strategic decisions. Both human analysts and AI systems inherit the blind spots of their information sources. We show that L…
Benchmarking Multimodal Models for Ukrainian Language Understanding Across Academic and Cultural Domains
Yurii Paniv, Artur Kiulian, Dmytro Chaplynskyi +4
While the evaluation of multimodal English-centric models is an active area of research with numerous benchmarks, there is a profound lack of benchmarks or evaluation suites for lo…
From English-Centric to Effective Bilingual: LLMs with Custom Tokenizers for Underrepresented Languages
Artur Kiulian, Anton Polishko, Mykola Khandoga +10
In this paper, we propose a model-agnostic cost-effective approach to developing bilingual base large language models (LLMs) to support English and any target language. The method…
Minimizing Live Experiments in Recommender Systems: User Simulation to Evaluate Preference Elicitation Policies
Chih-Wei Hsu, Martin Mladenov, Ofer Meshi +8
Evaluation of policies in recommender systems typically involves A/B testing using live experiments on real users to assess a new policy's impact on relevant metrics. This ``gold s…
From Bytes to Borsch: Fine-Tuning Gemma and Mistral for the Ukrainian Language Representation
Artur Kiulian, Anton Polishko, Mykola Khandoga +4
In the rapidly advancing field of AI and NLP, generative large language models (LLMs) stand at the forefront of innovation, showcasing unparalleled abilities in text understanding…