Artificial Intelligence

Large vision-language models (LVLMs) suffer from hallucination, generating responses that apparently contradict to the image content occasionally. The key problem lies in its weak ability to comprehend detailed content in multi-modal contexts, which can be mainly attributed its training data. The vision instruction dataset primarily focuses on global description that are highly relevant to the image, with few samples containing image details.

This dataset is a valuable resource for those working on daylight illumination-related projects. It includes a comprehensive collection of images captured under various daylight conditions. These images can be used for tasks such as illumination estimation, scene relighting, and object insertion. The dataset features scenes illuminated by different intensities and angles of daylight, providing a rich set of examples for realistic daylight simulation.

As shown in the figure 1, the NLP market is projected to grow from USD 31.76 billion in 2024 to USD 92.99 billion by 2029. This growth is driven by advances in deep learning and algorithms, increased digitization, and the integration of NLP with machine learning and deep learning. Key factors contributing to this expansion include the increasing use of NLP in healthcare and call centers, the demand for advanced text analytics, and growing machine-to-machine technology.

The dataset created focuses on the Pakistan Military by collecting five types of entities from Wikipedia: weapons, ranks, dates, operations, and locations. An open-source NER annotator was utilized for annotation, ensuring accurate labeling of data. Post-annotation, the data underwent cleaning and balancing processes. The final dataset comprises 660 neutral and 660 anti-military sentiment samples, totaling 1320 samples. This balanced dataset serves as a valuable resource for sentiment analysis, providing insights into public sentiment regarding military-related topics.