Junling Wang

The paper introduces a new benchmark, E2V-Bench, to evaluate text-to-image models on generating pedagogically accurate visuals from arithmetic equations, finding that current models often fail due to structural and numerical errors.

The paper proposes a novel layer and point-wise projection mapping combined with LoRA injection to efficiently distill knowledge from a large teacher model to a small student model, significantly improving performance while drastically reducing trainable parameters.

The paper introduces LFTutor, an intelligent tutoring system leveraging LLMs and Socratic questioning to teach laypeople about logical fallacies, demonstrating its effectiveness in fostering critical thinking against misinformation.