针对教育类填字游戏线索数据缺失问题，提出基于 Wikipedia 文本与 LLM 自动生成教学线索的方法，构建含 44,075 个样本的 clue-instruct 数据集，并通过人工与自动评估验证其有效性。
摘要：
填字游戏是广受欢迎的语言游戏，常被用作吸引学生参与学习的工具。教育类填字游戏的特点是线索不那么隐晦，而更偏重事实，这使其区别于传统填字游戏。尽管已经存在若干公开可用的传统填字游戏线索-答案对数据库，但教育类线索-答案对数据集仍然缺失。本文中，我们提出了一种构建教育类线索生成数据集的方法，该数据集可用于指导大语言模型（LLM）。通过从 Wikipedia 页面收集与相关关键词关联的信息性内容，我们利用大语言模型自动生成与给定输入关键词及其上下文相关的教学线索。借助这种方法，我们创建了 clue-instruct，这是一个包含 44,075 个唯一样本的数据集，其中文本-关键词对关联了三条不同的填字游戏线索。我们使用 clue-instruct 指导不同的 LLM 根据给定输入内容和关键词生成教育类线索。人工评估与自动评估均证实了所生成线索的质量，从而验证了我们方法的有效性。
英文摘要：
Crossword puzzles are popular linguistic games often used as tools to engage students in learning. Educational crosswords are characterized by less cryptic and more factual clues that distinguish them from traditional crossword puzzles. Despite there exist several publicly available clue-answer pair databases for traditional crosswords, educational clue-answer pairs datasets are missing. In this article, we propose a methodology to build educational clue generation datasets that can be used to instruct Large Language Models (LLMs). By gathering from Wikipedia pages informative content associated with relevant keywords, we use Large Language Models to automatically generate pedagogical clues related to the given input keyword and its context. With such an approach, we created clue-instruct, a dataset containing 44,075 unique examples with text-keyword pairs associated with three distinct crossword clues. We used clue-instruct to instruct different LLMs to generate educational clues from a given input content and keyword. Both human and automatic evaluations confirmed the quality of the generated clues, thus validating the effectiveness of our approach.
Red-teaming the Multimodal Reasoning: Jailbreaking Vision-Language Models via Cross-modal Entanglement Attacks
Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所）；University of Chinese Academy of Sciences（中国科学院大学）；People’s Public Security University of China（中国人民公安大学）
Emotion-LLaMA: Multimodal Emotion Recognition and Reasoning with Instruction Tuning
Shenzhen Technology University（深圳技术大学）；Carnegie Mellon University（卡内基梅隆大学）；Alibaba Group（阿里巴巴集团）；National University of Singapore（新加坡国立大学）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）
Structured Semantic Cloaking for Jailbreak Attacks on Large Language Models
Institute of High Performance Computing (IHPC), Agency for Science, Technology and Research (A*STAR), Singapore（高性能计算研究所（IHPC），科技研究局（A*STAR），新加坡）；Information Systems Technology and Design Pillar, Singapore University of Technology and Design（信息技术与设计支柱，新加坡科技设计大学）
