Parameter Efficient Multimodal Instruction Tuning for Romanian Vision Language Models

A new dataset named ClimateIQA has been introduced to enhance the capabilities of Vision-Language Models (VLMs) in analyzing meteorological anomalies. This dataset, which includes 26,280 high-quality images, aims to address the challenges faced by existing models like GPT-4o and Qwen-VL in interpreting complex meteorological heatmaps characterized by irregular shapes and color variations.

A new framework named RefVFX has been introduced, enabling the transfer of complex temporal effects from a reference video to a target video or image in a feed-forward manner. This innovation addresses challenges in dynamic temporal effects, such as lighting changes and character transformations, which are difficult to articulate through text or static conditions.

Qalb has been introduced as the largest state-of-the-art Urdu large language model, developed to address the underrepresentation of Urdu in modern natural language processing (NLP) systems. This model utilizes a two-stage approach involving continued pre-training on a dataset of 1.97 billion tokens, which includes diverse Urdu texts and English Wikipedia data.

A novel Price-Incentive Mechanism (PRINCE) has been proposed to enhance Multi-Tenant Split Federated Learning (SFL) for Foundation Models (FMs) like GPT-4, enabling efficient fine-tuning on resource-constrained devices while maintaining privacy. This mechanism addresses the coordination challenges faced by multiple SFL tenants with diverse fine-tuning needs.

A theoretical analysis of decentralization in autoregressive generation has been presented, introducing the Decentralized Discrete Flow Matching objective, which expresses probability generating velocity as a linear combination of expert flows. Experiments demonstrate the equivalence between decentralized and centralized training settings for multimodal language models, specifically comparing LLaVA and InternVL 2.5-1B.