Aligning Vision to Language: Annotation-Free Multimodal Knowledge Graph Construction for Enhanced LLMs Reasoning

The introduction of MultiPriv marks a significant advancement in the evaluation of individual-level privacy reasoning within Vision-Language Models (VLMs). This benchmark addresses the inadequacies of current privacy assessments, which primarily focus on privacy perception rather than the ability of VLMs to link distributed information and construct individual profiles. The framework includes a novel bilingual multimodal dataset that features synthetic individual profiles linked to sensitive attributes.

A new framework called MMT-ARD has been proposed to enhance the robustness of Vision-Language Models (VLMs) through a Multimodal Multi-Teacher Adversarial Distillation approach. This method addresses the limitations of traditional single-teacher distillation by incorporating a dual-teacher knowledge fusion architecture, which optimizes both clean feature preservation and robust feature enhancement.

A recent evaluation of large language models (LLMs) in academic library reference services examined their ability to provide equitable support across diverse user demographics, including sex, race, and institutional roles. The study found no significant differentiation in responses based on race or ethnicity, with only minor evidence of bias against women in one model. LLMs showed nuanced responses tailored to users' institutional roles, reflecting professional norms.

SPEAR-1 has been introduced as a significant advancement in the field of robotic foundation models, aiming to enhance the generalization capabilities of robots across diverse environments and tasks. This initiative addresses the limitations of existing models that primarily rely on 2D image-language tasks, which do not adequately support 3D spatial reasoning necessary for effective robotic control.

A novel learning framework utilizing Large Language Models (LLMs) has been introduced to enhance the generalization capabilities of Neural Combinatorial Optimization (NCO) for Vehicle Routing Problems (VRPs). This approach addresses the significant performance drop observed when NCO models trained on small-scale instances are applied to larger scenarios, primarily due to distributional shifts between training and testing data.

A recent study highlights the limitations of Large Language Models (LLMs) in understanding Tunisian Arabic, also known as Tunizi. This research introduces a new dataset that includes parallel translations in Tunizi, standard Tunisian Arabic, and English, aiming to benchmark LLMs on their comprehension of this low-resource language. The findings indicate that the neglect of such dialects may hinder millions of Tunisians from engaging with AI in their native language.

Recent research has examined whether Vision-Language Models (VLMs) comprehend visual persuasion, which influences human attitudes and decisions. A new dataset was created for binary persuasiveness judgment, introducing a taxonomy of Visual Persuasive Factors (VPFs) that includes various levels of visual cues. The analysis indicates that VLMs tend to overestimate high persuasiveness and struggle with low/mid-level features, while high-level semantic alignment is a strong predictor of human judgment.

A new benchmark named MUCH has been introduced to assess Claim-level Uncertainty Quantification (UQ) in Large Language Models (LLMs). This benchmark includes 4,873 samples in English, French, Spanish, and German, and provides 24 generation logits per token, enhancing the evaluation of UQ methods under realistic conditions.