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 LLMs, combined with prediction markets, function as a calibrated instrument for measuring how far ecosystem-induced beliefs fall from reality: LLMs extract the beliefs a text corpus implies, and prediction markets provide a ground truth proxy against which to quantify the error.We isolate the bias contribution of specific text through ablation: varying information context while holding the model fixed, with a contaminated model that knows actual outcomes as control. Applied to 111 Ukraine-related prediction markets (~93,000 predictions, four models), we find that English news context systematically biases territorial predictions, wrong 64–72% of the time (p 10{-6}). A contaminated model that knows actual outcomes shows the same error rate, indicating the bias originates primarily in the text. Supplementing with Ukrainian military-analytical sources partially corrects the distortion.We show that the distortion originates primarily in the sources, not the models. Consistent across four architectures, it will persist in any system that processes them and propagate into downstream decisions.

In this paper, we present our solutions for the two UNLP 2025 shared tasks: manipulation span detection and manipulation technique classification in Ukraine-related media content sourced from Telegram channels. We experimented with fine-tuning large language models (LLMs) with up to 12 billion parameters, including both encoder- and decoder-based architectures. Our experiments identified Gemma 3 12b with a custom classification head as the best-performing model for both tasks. To address the limited size of the original training dataset, we generated 50k synthetic samples and marked up an additional 400k media entries containing manipulative content.

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 includes vocabulary expansion, initialization of new embeddings, model training and evaluation. We performed our experiments with three languages, each using a non-Latin script—Ukrainian, Arabic, and Georgian.Our approach demonstrates improved language performance while reducing computational costs. It mitigates the disproportionate penalization of underrepresented languages, promoting fairness and minimizing adverse phenomena such as code-switching and broken grammar. Additionally, we introduce new metrics to evaluate language quality, revealing that vocabulary size significantly impacts the quality of generated text.

In decision making, generating alternative solutions is crucial for solving a problem. However, cognitive biases can impede this process by constraining individual decision makers’ creativity. To address this issue, we introduce a new task for automatically generating alternatives, inspired by the process of human “brainstorming”. We define alternative options based on atomic action components and present a dataset of 106 annotated Reddit r/Advice posts containing unique alternative options extracted from users’ replies. We also introduce new metrics to assess the quality of generated components, including distinctiveness, creativity, upvote-weighted, crowd intersection, and final commit intersection scores. As a baseline, we evaluated the large language models (LLMs) LLaMa3:8b, LLaMa3.1:8b, and Gemma 2:9b on the alternative component generation task. On the one hand, models demonstrated high creativity (ability to generate options beyond what Reddit users suggested) and performed well at proposing distinct alternatives. A subset of generated components was manually evaluated and found overall useful. This indicates that LLMs might be used to extend lists of alternative options, helping decision makers consider a problem from different perspectives. On the other hand, LLMs’ outputs often failed to align with human suggestions, implying that they still tend to miss important components.