Large language models (LLMs) have demonstrated strong performance on emotion understanding tasks, yet their ability to faithfully generate emotionally aligned text remains less well understood.We propose a semantic evaluation framework that jointly assesses emotion understanding, emotion generation, and internal consistency, using a VAE-based emotion cost matrix that captures graded semantic similarity between emotion categories.Our framework introduces four complementary metrics that disentangle baseline understanding, human-perceived emotion in generated text, generation quality, and model consistency.Experimental results show that while understanding and consistency scores are highly correlated, emotion generation exhibits substantially weaker correlations with these metrics.These findings motivate the development of specialized evaluation protocols that independently measure emotional understanding and generation, enabling more reliable assessments of LLM emotional intelligence.

The Iranic language family includes many underrepresented languages and dialects that remain largely unexplored in modern NLP research. We introduce APARSIN, a multi-variety benchmark covering 14 Iranic languages, dialects, and accents, designed for sentiment analysis and machine translation. The dataset includes both high and low-resource varieties, several of which are endangered, capturing linguistic variation across them. We evaluate a set of instruction-tuned Large Language Models (LLMs) on these tasks and analyze their performance across the varieties. Our results highlight substantial performance gaps between standard Persian and other Iranic languages and dialects, demonstrating the need for more inclusive multilingual and dialectally diverse NLP benchmarks.

Mathematical reasoning captures fundamental aspects of human cognitive ability. Although recent advances in LLMs have led to substantial improvements in automated mathematical problem solving, most existing benchmarks remain focused on English. As a result, robust mathematical reasoning remains a challenging and insufficiently explored capability for underrepresented languages including Persian. To address this gap, we introduce PMWP, the first dataset of 15K elementary-level Persian math word problems that supports both supervised training and evaluation of reasoning models. By expanding mathematical reasoning resources beyond English, PMWP contributes to the development of multilingual AI systems with stronger reasoning capabilities. In this work, we conduct a systematic evaluation of the Persian math word problem solving capabilities of different state-of-the-art LLMs. Our results indicate that DeepSeek-V3 exhibits reduced language bias when problem texts are translated into English, while Gemini-2.5-Flash achieves the highest equation value accuracy (72.02%) in Persian. In addition, we investigate parameter-efficient adaptation for equation generation by applying LoRA-based fine-tuning to LLaMA-3-8B and Qwen-2.5-7B. Our results show that, following fine-tuning, these openweight models achieve 91.65% and 92.53% exact equation match accuracy, respectively. Overall, our findings provide insights into the comparative strengths and limitations of proprietary and open-weight models for mathematical reasoning in Persian.

Understanding how cross-modal interactions influence unimodal and multimodal emotion recognition remains an open question in multimodal affective computing. This study presents a systematic empirical investigation of how multimodal inputs affect both unimodal and multimodal emotion recognition performance. Using the UniC dataset, which provides modality-specific and global multimodal annotations across text, audio, and visual modalities, we conduct experiments based on the Tensor Fusion Network (TFN) under unimodal, bi-modal, and tri-modal configurations. Results show that cross-modal interactions exert complex and asymmetric effects. While additional modalities can provide complementary emotional cues, they may also introduce interference when signals diverge. Models continue to struggle with less frequent or extreme emotions such as disgust. Notably, multimodal embeddings combined with unimodal annotations outperform fully multimodal supervision in the same setup, highlighting the role of annotation consistency and cue reliability. These findings provide a systematic empirical validation of the long-assumed notions, demonstrating that cross-modal effects are not simply additive and highlighting the need for more interpretable multimodal fusion strategies.

This article investigates the automatic explanation of irony in English tweets. The work covers the development and validation of a conceptual framework for annotating knowledge-informed explanations for figurative language as well as the training and evaluation of specialized generative models. Human judgements confirm that both fine-tuned open-source models (Llama 3) and proprietary models (GPT-4) can produce high-quality explanations, effectively incorporating relevant world knowledge. While metrics like BLUE and ROUGE do not seem to align with human judgement, we find that semantic similarity measures align well with human quality estimations. The resulting models and datasets for irony explanations, published as the iRONNIE collection, actively bridge the gap between theoretical understanding of irony and the technical innovations of the NLP domain. The models are be released to the public to facilitate a deeper linguistic analysis of world knowledge involved in understanding irony on social media in future work.