Papers Question Generation
“Question Generation” 태그가 달린 논문 773편 · 필터 해제
DiagEvo: Diagnosis-Guided Self-Evolution via Hierarchical Error Memory
Self-play is an effective paradigm for language-model self-evolution, but without guidance, solver performance can plateau or decline across rounds. Unguided methods steer question generation with signals such as difficu…
Mathematical ReasoningQuestion GenerationSPARK: Skeleton-Guided Reasoning Synthesis from Large-Scale Scientific Literature
Scientific reasoning remains challenging for open-source models, largely due to the lack of high-quality scientific reasoning data. Existing datasets are often dominated by factual recall or formulaic problem solving, wi…
Question GenerationBehavioral Reprogramming of Open-Weights Models: Cognitive Plasticity and Alignment Bounds
Large language models (LLMs) are predominantly aligned to function as passive, sycophantic assistants. We challenge this default paradigm by empirically evaluating the cognitive plasticity of open-weight architectures wh…
parameter-efficient fine-tuningQuestion GenerationTeachMateGPT: A Multi-Agent Knowledge-Grounded Framework for Pedagogical Assessment Generation from Science Curriculum Materials
Automatically generating textbook-grounded assessment items can reduce science teachers' workload, but existing retrieval-augmented generation (RAG) systems rely on flat retrieval, support only single-question generation…
Question GenerationExploiting Intrinsic Duality for Multi-Hop Question Generation
Multi hop question generation (MQG) aims to generate questions from multiple given documents and target answers, whereas question answering (QA) focuses on deriving answers from documents given specific questions. Althou…
Contrastive LearningQuestion GenerationQuestion AnsweringMMHBench: A Multi-Perspective Benchmark for Mental Health Understanding in Long-Form Videos
Mental health understanding in long-form videos requires nuanced reasoning over observable behavior, interpersonal context, and latent psychological states. Existing benchmarks largely reduce this task to coarse-grained …
Question GenerationChronoQG: Towards a Temporally Expressive and Hop-Bounded Benchmark for Temporal Knowledge Graph Question Generation
Knowledge graph question generation (KGQG) aims to generate natural-language questions from structured graph evidence. Existing KGQG benchmarks, however, are mostly built on static knowledge graphs and do not encode the …
Question GenerationKnowledge GraphsSelf-Study Reconsidered: The Hidden Fragility of Learning from Self-Generated QA
Language models are increasingly taught from synthetic question--answer (QA) supervision: a model generates questions about a document, answers them from the same text, and the resulting pairs are used to fine-tune, dist…
Question GenerationNuclearQAv2: A Structured Benchmark for Evaluating Domain-Science Competence in Large Language Models
Large language models (LLMs) have demonstrated strong performance across a wide range of tasks, but ensuring their reliability in highly technical domains remains a significant challenge. In nuclear engineering, problem …
Question GenerationLLM-as-Judge in Education: A Curriculum-Grounded Marking Pipeline
Generative AI and large language models (LLMs) are increasingly applied to question generation and automated assessment. However, deploying LLMs in preparation for high-stakes exams requires more than prompt engineering;…
Question GenerationPrompt EngineeringERQA-Plus: A Diagnostic Benchmark for Reasoning in Embodied AI
Generalist embodied agents require more than object recognition: they must reason about spatial relations, actions, procedures, human intentions, environmental constraints, and commonsense consequences from situated visu…
Question GenerationObject RecognitionQuestion AnsweringSpatial ReasoningVeriGeo: Controllable Geometry Question Generation with Numerical and Analytical Verification
Geometry problem generation is useful for AI-assisted education and multimodal mathematical reasoning, but reliable synthesis remains difficult because the problem statement, diagram, constraints, and solution should be …
Mathematical ReasoningQuestion GenerationConstructing Evaluation Datasets for Procedural Reasoning: Balancing Naturalness, Grounding, and Multi-Hop Coverage
Evaluating procedural reasoning in AI-supported learning systems requires question-answer datasets that are both learner-like and grounded in the instructional knowledge the system is expected to use. We study how TMK-ba…
Question GenerationHow Fine-Grained Should a RAG Benchmark Be? A Hierarchical Framework for Synthetic Question Generation
Evaluating retrieval-augmented generation (RAG) systems requires benchmarks that capture diverse question characteristics, yet practitioners lack empirical guidance on which dimensions to vary and at what granularity. We…
Question GenerationSelf-Evolving Visual Questioner
Vision-language models (VLMs) are typically trained as passive answerers, while their ability to actively ask diverse, non-trivial, visual-centric and grounded questions remains underexplored. Existing visual questioners…
Question GenerationWhat Am I Missing? Question-Answering as Hidden State Probing
Test-time reasoning has become a significant field of study since the introduction of chain-of-thought reasoning in large language models (LLMs). However, the mechanisms of this reasoning process are still under-explored…
Question GenerationSlide Deck Q&A Quality Assurance App: A Multi-Stage Pipeline for Pedagogical Question Generation
Generating high-quality, pedagogically useful questions from lecture slide decks is difficult because important instructional content is distributed across both text and visual elements, and because useful questions must…
Question GenerationTS-Skill: A Benchmark for Evaluating Analytical Skills in Time-Series Question Answering
Large language models (LLMs) and time-series language models (TSLMs) are increasingly applied to time-series question answering (TSQA). Unlike text-only QA, TSQA requires models to ground answers in temporal signals whos…
Question GenerationQuestion AnsweringEvoVid: Temporal-Centric Self-Evolution for Video Large Language Models
Recent Video Large Language Models (Video-LLMs) have demonstrated strong capabilities in video reasoning through reinforcement learning (RL). However, existing RL pipelines rely heavily on human-annotated tasks and solut…
Reinforcement LearningQuestion GenerationRISE: Reliable Improvement in Self-Evolving Vision-Language Models
Vision-language models (VLMs) have achieved strong multimodal reasoning capabilities, but further improving them still relies heavily on large-scale human-constructed supervision for post-training. Such supervision is co…
Multimodal ReasoningQuestion Generation