8 papers
Histogram-constrained Image Generation
Haoming Liu, Yuanhe Guo, Yijia Cao +2
Diffusion models have emerged as a dominant paradigm in generative modeling, enabling high-fidelity sampling from complex data distributions. Despite impressive capabilities, contr…
Evaluating Interactivity: Toward Automated Assessment of AI-Generated Explorable Explanations
Xiaozao Wang, Zhewei Wang, Hongyi Wen
While large language models now enable rapid generation of interactive learning materials, evaluating the interaction quality of these explorable explanations remains an open chall…
Diagnosing Knowledge Gaps in LLM Tool Use: An Agentic Benchmark for Novel API Acquisition
Jinnuo Liu, Yue Peng, Jinhan Niu +1
Large language models for code generation often need to use APIs that are absent from their pretraining data. This requires more than recalling a function name: models must coordin…
SHAPE: Unifying Safety, Helpfulness and Pedagogy for Educational LLMs
Sihang Zhao, Kangrui Yu, Youliang Yuan +2
Large Language Models (LLMs) have been widely explored in educational scenarios. We identify a critical vulnerability in current educational LLMs, pedagogical jailbreaks, where stu…
From Navigation to Refinement: Revealing the Two-Stage Nature of Flow-based Diffusion Models through Oracle Velocity
Haoming Liu, Jinnuo Liu, Yanhao Li +5
Flow-based diffusion models have emerged as a leading paradigm for training generative models across images and videos. However, their memorization-generalization behavior remains…
An Analysis of Large Language Models for Simulating User Responses in Surveys
Ziyun Yu, Yiru Zhou, Chen Zhao +1
Using Large Language Models (LLMs) to simulate user opinions has received growing attention. Yet LLMs, especially trained with reinforcement learning from human feedback (RLHF), ar…