2026
Counterfactual Question Generation Uncovering Learner Contradictions
AAAI 2026technical
Conventional feedback, even when accompanied by brief explanations, rarely uncovers the hidden contradictions that trigger a learner
4 accepted papers
Conventional feedback, even when accompanied by brief explanations, rarely uncovers the hidden contradictions that trigger a learner
LLM-to-KG systems frequently fail on exclusion-rich questions because natural-language negation is both scope-sensitive and evidence-dependent: it may constrain only one subgoal/branch and only certain supporting paths, yet such attachment is rarely explicit in text. We propose the Executable Exchan…
Multimodal Sentiment Analysis (MSA) with missing modalities has attracted increasing attention recently. While current Transformer-based methods leverage dense text information to maintain model robustness, their quadratic complexity hinders efficient long-range modeling and multimodal fusion. To th
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 ques…