CARE-MI: Chinese Benchmark for Misinformation Evaluation in Maternity and Infant Care

The recent advances in NLP, have led to a new trend of applying LLMs to real-world scenarios. While the latest LLMs are astonishingly fluent when interacting with humans, they suffer from the misinformation problem by unintentionally generating factually false statements. This can lead to harmful consequences, especially when produced within sensitive contexts, such as healthcare. Yet few previous works have focused on evaluating misinformation in the long-form generation of LLMs, especially for knowledge-intensive topics. Moreover, although LLMs have been shown to perform well in different languages, misinformation evaluation has been mostly conducted in English. To this end, we present a benchmark, CARE-MI, for evaluating LLM misinformation in: 1) a sensitive topic, specifically the maternity and infant care domain; and 2) a language other than English, namely Chinese. Most importantly, we provide an innovative paradigm for building long-form generation evaluation benchmarks that can be transferred to other knowledge-intensive domains and low-resourced languages. Our proposed benchmark fills the gap between the extensive usage of LLMs and the lack of datasets for assessing the misinformation generated by these models. It contains 1,612 expert-checked questions, accompanied with human-selected references. Using our benchmark, we conduct extensive experiments and found that current Chinese LLMs are far from perfect in the topic of maternity and infant care. In an effort to minimize the reliance on human resources for performance evaluation, we offer a judgment model for automatically assessing the long-form output of LLMs using the benchmark questions. Moreover, we compare potential solutions for long-form generation evaluation and provide insights for building more robust and efficient automated metric

Altered brain functional network topology in Obsessive-Compulsive Disorder: A comparison of patients with varying severity of depressive symptoms and the impact on psychosocial functioning

Background: Obsessive-compulsive disorder (OCD) is associated with psychosocial impairment, which can be exacerbated by depressive symptoms. In this study, we employed graph theory analysis to investigate the association among neuroimaging, clinical features, and psychosocial functioning in OCD patients, with a specific focus on the differential impact of depressive symptoms. Methods: 216 OCD patients were divided into two subgroups based on depressive symptoms. Resting-state functional MRI data were acquired from a subset of 106 OCD patients along with 77 matched healthy controls (HCs). We analyzed the topological characteristics of the entire brain and the cognition-related subnetworks and performed Pearson correlation analyses to further explore the relationship with psychosocial functioning. Results: OCD patients with more severe depressive symptoms exhibited greater impairment across all dimensions of psychosocial functioning. Graph theory analysis revealed more pronounced reductions in network efficiency within the entire brain, the default mode network (DMN), and the cingulo-opercular network (CON) among patients with non or mild depressive symptoms. Lower nodal efficiency and degree centrality of the right superior temporal gyrus (STG) were found in OCD patients and these variables were positively correlated with psychosocial functioning impairment. Conclusions: This study revealed that the presence of depressive symptoms generally exacerbated psychosocial functioning impairment in OCD patients. Abnormalities in the functional integration of the entire brain, the DMN, and the CON in OCD patients may comprise the basis of cognitive deficits, while dysfunction of the right STG may affect the psychosocial functioning through its role in emotion, intention perception, and insight

Spatial association of surface water quality and human cancer in China

Abstract Little is known about the association between surface water quality and cancer incidence, especially in China. Drinking water quality has been linked to the incidence of several cancers in individual-level studies. However, few studies have attempted to examine multiple pollutants and multiple cancers at population level. This study used water monitoring and population-level cancer data from across China to examine spatial associations between water pollutants and types of cancer. We found a “dose–response” relationship between the number of pollutants present at high levels and cancer incidence. These results provide evidence of a nationwide spatial association between water quality and cancer in China. The precise relationship varies with cancers and pollutants. However, the overall consistency of the “dose–response” relationship suggests that surface water quality is an important factor in cancer incidence. Our findings highlight new issues such as the changing effects when different pollutants co-exist and an increasing number of new cancer cases partially attributable to poor water quality. Our work also points to some ways to deal with these challenges