Daniil Orel

Stereotype bias in language models has been widely examined in English, but remains largely understudied in bilingual contexts where multiple linguistic and cultural systems interact. This gap is especially important in regions where language use reflects complex historical and sociopolitical inf...

Multi-step symbolic reasoning is essential for robust financial analysis; yet, current benchmarks largely overlook this capability. Existing datasets such as FinQA and ConvFinQA emphasize final numerical answers while neglecting the intermediate reasoning steps required for transparency and verif...

News outlets shape public opinion on a scale, which makes automated detection of political bias and factuality essential. Yet, the field still lacks unified resources, comprehensive evaluations in diverse approaches, and systematic analyzes of the representations and fusion strategies that matter...

Reasoning is a core capability of large language models (LLMs), yet how multi-step reasoning is learned and executed remains unclear. We study this question in a controlled cellular-automata (1dCA) framework that excludes memorization by using disjoint training and test rules. Given a short state...