Knowledge-based questions are typically employed to evaluate LLM's knowledge boundaries; meanwhile, numerous studies focus on question generation as a means to enhance the capabilities of both models and individuals. However, there is a lack of in-depth exploration about what constitutes a good question from the perspective of knowledge cognition. This paper proposes aligning the complete knowledge underlying questions with educational criteria effectively employed in physics courses, thereby developing novel knowledge-intensive metrics of question quality. To this end, we propose Meta-Fact Checking (MFC), which transforms questions into knowledge graph (KG) triples utilizing LLMs through few-shot prompting, thereby quantifying question quality based on the patterns observed within these triples. MFC introduces a novel interaction mechanism for KGs that communicates meta-facts, illustrating the types of knowledge that KGs can offer to the LLM for reasoning questions, rather than relying solely on the original triples. This strategy ensures that MFC remains unaffected by unexplored triples that LLM has not yet encountered within KGs compared to the retrieve-while-reasoning routine. Experiments across multiple datasets and LLMs demonstrate that MFC significantly improves the accuracy and efficiency of both question answering and assessing. This research marks a pioneering effort to automate the evaluation of question quality based on cognitive capabilities.

Crowd counting is getting more and more attention in our daily life, because it can effectively prevent some safety problems. However, due to scale variations and background noise in the image, such as buildings and trees, getting the accurate number from image is a hard work. In order to address these problems, this work introduces a new multi‐scale supervised network. The proposed model uses part of vgg16 model as the backbone to extract feature. In the training process, a multi‐scale dilated convolution module is added at the end of each stage of the backbone network to generate attention map with different resolutions to help the model focus on the head area in feature map. In addition, the dilated convolution adopts three dilation ratios to fit different sizes of head in the image. Finally, in order to get the high‐quality density map with high‐resolution, the authors employ the upsampling operation to restore the density map size to the quarter size of original image. A large number of experiments on these four datasets show that the proposed network has greatly improved the counting accuracy of many existing methods.

Estimating the total number of people in a crowded situation is a challenging task due to numerous occlusions and perspective changes existing in crowd images. To address this issue, the authors have proposed a new deep learning framework for accurate and efficient crowd counting here. Inspired by multi‐column convolutional neural network (MCNN) and contextual pyramid convolutional neural network (CP‐CNN), the authors use a combination of a two branches, convolutional neutral network (CNN) and transposed convolutional layers, to generate a high‐quality density map. The two‐branch CNN for feature extraction generates a density map that is only a quarter of size of the original image Then a set of transposed convolutional layers and convolutional layers are combined with the network to make up for the detail loss of the density map conducted by stacked pooling. Compared with MCNN and CP‐CNN, the authors’ approach employs fewer branches and simpler architecture. Experimental result shows that their approach achieves MAE 80.7 and MSE 131.2 in ShanghaiTech PartA dataset, MAE 15.6 and MSE 26.8 in ShanghaiTech PartB dataset, and MAE Average 7.1 in WorldExpo'10 dataset.