Multimodal large language models (MLLMs) are currently at the center of research attention, showing rapid progress in scale and capabilities, yet their intelligence, limitations, and risks remain insufficiently understood. To address these issues, particularly in the context of the Russian language, where no multimodal benchmarks currently exist, we introduce MERA Multi, an open multimodal evaluation framework for Russian-spoken architectures. The benchmark is instruction-based and encompasses default text, image, audio, and video modalities, comprising 18 newly constructed evaluation tasks for both general-purpose models and modality-specific architectures (image-to-text, video-to-text, and audio-to-text). Our contributions include: (i) a universal taxonomy of multimodal abilities; (ii) 18 datasets created entirely from scratch with attention to Russian cultural and linguistic specificity, unified prompts, and metrics; (iii) baseline results for both closed-source and open-source models; (iv) a methodology for preventing benchmark leakage, including watermarking for private sets. While our current focus is on Russian, the proposed benchmark provides a replicable methodology for constructing multimodal benchmarks in typologically diverse languages, particularly within the Slavic language family.

Frequent revisions of complex regulatory documents in large organizations often introduce inconsistencies and contradictions that are difficult for lawyers and auditors to detect manually. Existing tools rely on character-level diffs and therefore miss paraphrases and semantic shifts. We introduce LegDiff, a novel benchmark for evaluating span-aware semantic comparison of legal texts, and use it to investigate the ability of large language models to detect semantic changes beyond token- and character-level matching. LegDiff comprises manually annotated pairs of legal paragraphs drawn from different documents. In addition, we present a pipeline to generate synthetic training data that aligns with the manual annotations and mirrors the structure and label distribution of the manually curated benchmark, and a visualization tool for clearly displaying detected differences and inconsistencies. The dataset, code, and a visualization tool are publicly available to facilitate reproducibility and further research (https://github.com/s-nlp/SLeDoC).

We present a deployed system that automates end-to-end customer support workflows inside an enterprise Business Process Management (BPM) platform. The approach is scalable in production and reaches selective automation within two weeks for a new process, leveraging supervision already generated at scale: structured per-case UI interaction traces and low-overhead copilot feedback, where operators either accept a suggestion or provide a correction. A staged deployment pipeline trains a next UI action policy, learns a critic from copilot feedback to calibrate abstention, and executes only high-confidence steps in the background while deferring uncertain decisions to operators and resuming from the updated UI state. This setup lets one operator supervise multiple concurrent sessions and be interrupted only when the system is uncertain. The system operates on a schema-driven view of the BPM interface and includes monitoring and safe fallbacks for production. In production, it automated 45% of sessions and reduced average handling time by 39% without degrading support quality level.

The Multilingual shared-task on Hallucinations and Related Observable Overgeneration Mistakes in the SemEval-2025 competition aims to detect hallucination spans in the outputs of instruction-tuned LLMs in a multilingual context. In this paper, we address the detection of span hallucinations by applying an ensemble of approaches. In particular, we synthesized a PsiloQA dataset and fine-tuned LLM to detect hallucination spans. In addition, we combined this approach with a white-box method based on uncertainty quantification techniques. Using our combined pipeline, we achieved 3rd place in detecting span hallucinations in Arabic, Catalan, Finnish, Italian, and ranked within the top ten for the rest of the languages.

Measuring how real images look is a complex task in artificial intelligence research. For example, an image of Albert Einstein holding a smartphone violates common-sense because modern smartphone were invented after Einstein’s death. We introduce a novel method, which we called Through the Looking Glass (TLG), to assess image common sense consistency using Large Vision-Language Models (LVLMs) and Transformer-based encoder. By leveraging LVLM to extract atomic facts from these images, we obtain a mix of accurate facts. We proceed by fine-tuning a compact attention-pooling classifier over encoded atomic facts. Our TLG has achieved a new state-of-the-art performance on the WHOOPS! and WEIRD datasets while leveraging a compact fine-tuning component.

Hallucination detection remains a fundamental challenge for the safe and reliable deployment of large language models (LLMs), especially in applications requiring factual accuracy. Existing hallucination benchmarks often operate at the sequence level and are limited to English, lacking the fine-grained, multilingual supervision needed for comprehensive evaluation. In this work, we introduce PsiloQA, a large-scale, multilingual dataset annotated with span-level hallucinations across 14 languages. PsiloQA is constructed through an automated three-stage pipeline: generating question–answer pairs from Wikipedia using GPT-4o, eliciting potentially hallucinated answers from diverse LLMs in a no-context setting, and automatically annotating hallucinated spans using GPT-4o by comparing against golden answers and retrieved context. We evaluate a wide range of hallucination detection methods-including uncertainty quantification, LLM-based tagging, and fine-tuned encoder models-and show that encoder-based models achieve the strongest performance across languages. Furthermore, PsiloQA demonstrates effective cross-lingual generalization and supports robust knowledge transfer to other benchmarks, all while being significantly more cost-efficient than human-annotated datasets. Our dataset and results advance the development of scalable, fine-grained hallucination detection in multilingual settings.

Large Language Models (LLMs) often generate hallucinations, a critical issue in domains like scientific communication where factual accuracy and fluency are essential. The SHROOM-CAP shared task addresses this challenge by evaluating Factual Mistakes and Fluency Mistakes across diverse languages, extending earlier SHROOM editions to the scientific domain. We present Smurfcat, our system for SHROOM-CAP, which integrates three complementary approaches: uncertainty estimation (white-box and black-box signals), encoder-based classifiers (Multilingual Modern BERT), and decoder-based judges (instruction-tuned LLMs with classification heads). Results show that decoder-based judges achieve the strongest overall performance, while uncertainty methods and encoders provide complementary strengths. Our findings highlight the value of combining uncertainty signals with encoder and decoder architectures for robust, multilingual detection of hallucinations and related errors in scientific publications.

In this paper, we present our novel systems developed for the SemEval-2024 hallucination detection task. Our investigation spans a range of strategies to compare model predictions with reference standards, encompassing diverse baselines, the refinement of pre-trained encoders through supervised learning, and an ensemble approaches utilizing several high-performing models. Through these explorations, we introduce three distinct methods that exhibit strong performance metrics. To amplify our training data, we generate additional training samples from unlabelled training subset. Furthermore, we provide a detailed comparative analysis of our approaches. Notably, our premier method achieved a commendable 9th place in the competition’s model-agnostic track and 20th place in model-aware track, highlighting its effectiveness and potential.

We present RuDSI, a new benchmark for word sense induction (WSI) in Russian. The dataset was created using manual annotation and semi-automatic clustering of Word Usage Graphs (WUGs). RuDSI is completely data-driven (based on texts from Russian National Corpus), with no external word senses imposed on annotators. We present and analyze RuDSI, describe our annotation workflow, show how graph clustering parameters affect the dataset, report the performance that several baseline WSI methods obtain on RuDSI and discuss possibilities for improving these scores.