When evaluating a learner's knowledge proficiency, the multiple-choice question is an efficient and widely used format in standardized tests. Nevertheless, generating these questions, particularly plausible distractors (incorrect options), poses a considerable challenge. Generally, the distractor generation can be classified into cloze-style distractor generation (CDG) and natural questions distractor generation (NQDG). In contrast to the CDG, utilizing pre-trained language models (PLMs) for NQDG presents three primary challenges: (1) PLMs are typically trained to generate ``correct'' content, like answers, while rarely trained to generate ``plausible" content, like distractors; (2) PLMs often struggle to produce content that aligns well with specific knowledge and the style of exams; (3) NQDG necessitates the model to produce longer, context-sensitive, and question-relevant distractors. In this study, we introduce a fine-tuning framework named DGRC for NQDG in Chinese multi-choice reading comprehension from authentic examinations. DGRC comprises three major components: hard chain-of-thought, multi-task learning, and generation mask patterns. The experiment results demonstrate that DGRC significantly enhances generation performance, achieving a more than 2.5-fold improvement in BLEU scores.

The National Conference on Digital Government Research, being held May 19-21 in Boston this year, brings together more than 200 academic and government participants and features digital government research partnerships between the nation's top computer, information, social, organizational and political scientists, and federal, state and local government program managers from the United States and abroad. With nearly 100 papers, panels, case studies and live demonstrations, this year's conference is the largest to date. Nicholas Negroponte, co-founder of the MIT Media Laboratory, sets the tone for dg.o2003 with a keynote presentation Monday at 8 a.m. NSF's Digital Government program targets advances in government-citizen interaction, improves government agency applications, conducts related information technology research and examines the impact of information technology on democratic processes. The following Digital Government projects are among the many to be demonstrated at dg.o2003: UrbanSim: How does a city grow?