Cochrane systematic reviews evaluate the effectiveness and safety of medical interventions. Patients can benefit from clinicians’ integration of outcomes of these reviews into their daily practices. However, systematic reviews are usually long documents; even their abstracts can extend to 1000 words, making rapid appraisal challenging for busy health professionals. Large language models (LLMs) offer potential to further distil these abstracts. Nevertheless, generating high-quality, clinician-oriented summaries in this context is non-trivial. They must comprehensively cover the original abstract, while remaining accurate and professionally acceptable, i.e., retaining all clinically important details. To address this challenge, we have developed a novel dataset, PsycSumEval, comprising summaries generated by four different LLMs for 115 Cochrane abstracts concerning mental health. Psychiatrists evaluated each summary across nine content dimensions, assigning scores and providing free-text justifications that highlight inaccuracies and missing details. The corpus provides fine-grained insight into how psychiatrists assess professional acceptability of compressed medical evidence. Rather than treating agreement as a merely statistical endpoint, we capture structured expert judgments alongside their rationales, enabling transparent analysis of where professional norms are stable and where interpretive latitude persists. We contribute both a rigorous evaluation dataset and an explicit model of expert acceptability criteria for medical evidence summarisation.

Real-world financial analysis involves information across multiple languages and modalities, from reports and news to scanned filings and meeting recordings. Yet most existing evaluations of LLMs in finance remain text-only, monolingual, and largely saturated by current models. To bridge these gaps, we present MultiFinBen, the first expert-annotated multilingual (five languages) and multimodal (text, vision, audio) benchmark for evaluating LLMs in realistic financial contexts. MultiFinBen introduces two new task families: multilingual financial reasoning, which tests cross-lingual evidence integration from filings and news, and financial OCR, which extracts structured text from scanned documents containing tables and charts. Rather than aggregating all available datasets, we apply a structured, difficulty-aware selection based on advanced model performance, ensuring balanced challenge and removing redundant tasks. Evaluating 21 leading LLMs shows that even frontier multimodal models like GPT-4o achieve only 46.01% overall, stronger on vision and audio but dropping sharply in multilingual settings. These findings expose persistent limitations in multilingual, multimodal, and expert-level financial reasoning. All datasets, evaluation scripts, and leaderboards are publicly released.

Despite Greece’s pivotal role in the global economy, large language models (LLMs) remain underexplored for Greek financial context due to the linguistic complexity of Greek and the scarcity of domain-specific datasets. While multilingual financial NLP has revealed large performance gaps across languages, no benchmarks or LLMs have been tailored for Greek financial tasks until now. To bridge this gap, we introduce Plutus-ben, the first Greek Financial Evaluation Benchmark, and Plutus-8B, the first financial LLM fine-tuned on Greek-specific financial data. Plutus-ben addresses six core tasks: numeric/textual named entity recognition, question answering, extractive summarization, abstractive summarization, and topic classification. To support these tasks, we release four new expert-annotated Greek financial datasets and incorporate two existing resources. Our comprehensive evaluation of 24 LLMs reveals persistent challenges in Greek financial NLP, driven by linguistic complexity, domain terminology, and financial reasoning gaps. Experiment results underscore the limitations of cross-lingual transfer and the need for Greek-specific financial modeling. We publicly release Plutus-ben, Plutus-8B, and all associated datasets to promote reproducible research and advance multilingual financial NLP.

Social media platforms generate an enormous volume of multi-modal data, yet stress detection research has predominantly relied on text-based analysis. In this work, we propose a novel framework that integrates textual content with synthesized visual cues to enhance stress detection. Using the generative model DALL·E, we synthesize images from social media posts, which are then fused with text through the multi-modal capabilities of a pre-trained CLIP model. Our approach is evaluated on the Dreaddit dataset, where a classifier trained on frozen CLIP features achieves 94.90% accuracy, and full fine-tuning further improves performance to 98.41%. These results underscore the integration of synthesized visuals with textual data not only enhances stress detection but also offers a robust method over traditional text-only methods, paving the way for innovative approaches in mental health monitoring and social media analytics.