Training Generative Question-Answering on Synthetic Data Obtained from an Instruct-tuned Model

This paper presents a simple and cost-effective method for synthesizing data to train question-answering systems. For training, fine-tuning GPT models is a common practice in resource-rich languages like English, however, it becomes challenging for non-English languages due to the scarcity of sufficient question-answer (QA) pairs. Existing approaches use question and answer generators trained on human-authored QA pairs, which involves substantial human expenses. In contrast, we use an instruct-tuned model to generate QA pairs in a zero-shot or few-shot manner. We conduct experiments to compare various strategies for obtaining QA pairs from the instruct-tuned model. The results demonstrate that a model trained on our proposed synthetic data achieves comparable performance to a model trained on manually curated datasets, without incurring human costs.Comment: PACLIC 2023 short paper, 4 pages (6 pages including references), 4 figure

Learning to Select, Track, and Generate for Data-to-Text

We propose a data-to-text generation model with two modules, one for tracking and the other for text generation. Our tracking module selects and keeps track of salient information and memorizes which record has been mentioned. Our generation module generates a summary conditioned on the state of tracking module. Our model is considered to simulate the human-like writing process that gradually selects the information by determining the intermediate variables while writing the summary. In addition, we also explore the effectiveness of the writer information for generation. Experimental results show that our model outperforms existing models in all evaluation metrics even without writer information. Incorporating writer information further improves the performance, contributing to content planning and surface realization.Comment: ACL 201

The Discovery of LOX-1, its Ligands and Clinical Significance

LOX-1 is an endothelial receptor for oxidized low-density lipoprotein (oxLDL), a key molecule in the pathogenesis of atherosclerosis.The basal expression of LOX-1 is low but highly induced under the influence of proinflammatory and prooxidative stimuli in vascular endothelial cells, smooth muscle cells, macrophages, platelets and cardiomyocytes. Multiple lines of in vitro and in vivo studies have provided compelling evidence that LOX-1 promotes endothelial dysfunction and atherogenesis induced by oxLDL. The roles of LOX-1 in the development of atherosclerosis, however, are not simple as it had been considered. Evidence has been accumulating that LOX-1 recognizes not only oxLDL but other atherogenic lipoproteins, platelets, leukocytes and CRP. As results, LOX-1 not only mediates endothelial dysfunction but contributes to atherosclerotic plaque formation, thrombogenesis, leukocyte infiltration and myocardial infarction, which determine mortality and morbidity from atherosclerosis. Moreover, our recent epidemiological study has highlighted the involvement of LOX-1 in human cardiovascular diseases. Further understandings of LOX-1 and its ligands as well as its versatile functions will direct us to ways to find novel diagnostic and therapeutic approaches to cardiovascular disease

Shared decision-making in physiotherapy: a cross-sectional study of patient involvement factors and issues in Japan

Abstract Background Evidence-based medicine education has not focused on how clinicians involve patients in decision-making. Although shared decision-making (SDM) has been investigated to address this issue, there are insufficient data on SDM in physiotherapy. This study aimed to clarify the issues concerning patient involvement in Japan, and to examine whether SDM is related to perceptions of patient involvement in decision-making. Methods The study participants were recruited from among acute and sub-acute inpatients and community residents receiving physiotherapy outpatient care, day care, and/or home rehabilitation. The Control Preference Scale (CPS) was used to measure the patients' involvement in decision-making. The nine-item Shared Decision-Making Questionnaire (SDM-Q-9) was used to measure SDM. In analysis I, we calculated the weighted kappa coefficient to examine the congruence in the CPS between the patients' actual and preferred roles. In analysis II, we conducted a logistic regression analysis using two models to examine the factors of patient involvement. Results Analysis I included 277 patients. The patients' actual roles were as follows: most active (4.0%), active (10.8%), collaborative (24.6%), passive (35.0%), and most passive (25.6%). Their preferred roles were: most active (3.3%), active (18.4%), collaborative (39.4%), passive (24.5%), and most passive (14.4%). The congruence between actual and preferred roles by the kappa coefficient was 0.38. Analysis II included 218 patients. The factors for patient involvement were the clinical environment, the patient's preferred role, and the SDM-Q-9 score. Conclusions The patients in Japan indicated a low level of decision-making involvement in physiotherapy. The patients wanted more active involvement than that required in the actual decision-making methods. The physiotherapist's practice of SDM was revealed as one of the factors related to perceptions of patient involvement in decision-making. Our results demonstrated the importance of using SDM for patient involvement in physiotherapy